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Biosensors
Volume 12
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Biosensors, Volume 12, Issue 12 (December 2022) – 124 articles

Cover Story (view full-size image): Helicobacter pylori is a microaerophilic, gastric, cancer-causing bacterium and develops colonization in gastric environments with the help of a major virulence factor, CagA (cytotoxin-associated gene A). In order to detect CagA, a nanomaterials-based molecularly imprinted sensing surface was fabricated by using CagA as a template. Pre-polymerization conditions were optimized through molecular dynamics simulations to obtain well-matched optimized molar ratios of monomers, cross-linkers, and templates. A simulation study revealed that a low binding energy was obtained upon template removal, which indicates the capability of MIP to recognize the CagA antigen through a strong binding affinity. Under the optimized electrochemical experimental conditions, the fabricated CagA-MIP/Au/rGO@SPE sensor exhibited high sensitivity and a low limit of detection. View this paper
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37 pages, 3405 KiB  
Review
The Roadmap of Graphene-Based Sensors: Electrochemical Methods for Bioanalytical Applications
by Ghazala Ashraf, Ayesha Aziz, Tayyaba Iftikhar, Zi-Tao Zhong, Muhammad Asif and Wei Chen
Biosensors 2022, 12(12), 1183; https://doi.org/10.3390/bios12121183 - 19 Dec 2022
Cited by 10 | Viewed by 3342
Abstract
Graphene (GR) has engrossed immense research attention as an emerging carbon material owing to its enthralling electrochemical (EC) and physical properties. Herein, we debate the role of GR-based nanomaterials (NMs) in refining EC sensing performance toward bioanalytes detection. Following the introduction, we briefly [...] Read more.
Graphene (GR) has engrossed immense research attention as an emerging carbon material owing to its enthralling electrochemical (EC) and physical properties. Herein, we debate the role of GR-based nanomaterials (NMs) in refining EC sensing performance toward bioanalytes detection. Following the introduction, we briefly discuss the GR fabrication, properties, application as electrode materials, the principle of EC sensing system, and the importance of bioanalytes detection in early disease diagnosis. Along with the brief description of GR-derivatives, simulation, and doping, classification of GR-based EC sensors such as cancer biomarkers, neurotransmitters, DNA sensors, immunosensors, and various other bioanalytes detection is provided. The working mechanism of topical GR-based EC sensors, advantages, and real-time analysis of these along with details of analytical merit of figures for EC sensors are discussed. Last, we have concluded the review by providing some suggestions to overcome the existing downsides of GR-based sensors and future outlook. The advancement of electrochemistry, nanotechnology, and point-of-care (POC) devices could offer the next generation of precise, sensitive, and reliable EC sensors. Full article
(This article belongs to the Special Issue Construction of Biosensors Using Nano- and Microtechnology)
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>A</b>) 2D GR as the primary constituent of various carbon−based materials. (<b>B</b>) A simplistic representation of various components involved in detecting bioanalytes using EC sensor and related transducer responses.</p> Full article ">Figure 2
<p>(<b>A</b>) Structural demonstration of various GR derivatives. (<b>B</b>) A simple depiction of an SPCE and mechanism of EC sulfide oxidation on the electrode surface. Reprinted with permission from Ref. [<a href="#B54-biosensors-12-01183" class="html-bibr">54</a>].</p> Full article ">Figure 3
<p>(<b>A</b>) Schematic representation of the [MnO<sub>6</sub>] octahedral unit’s fundamental structure (<b>a</b>), perfectly aligned [MnO<sub>2</sub>] nanorods (<b>b</b>), Mn vacancies in [MnO<sub>2</sub>] nanorods (<b>c</b>), and Ag doping in [MnO<sub>2</sub>] nanorods (<b>d</b>). Reprinted with permission from [<a href="#B79-biosensors-12-01183" class="html-bibr">79</a>]. (<b>B</b>) NdFeO<sub>3</sub> fabrication using a mixed oxide method in (<b>a</b>), and its molecular structure, and (<b>b</b>) modified NdFeO<sub>3</sub> electrodes can be used to detect H<sub>2</sub>O<sub>2</sub>, (c) GCE modification for the detection of H<sub>2</sub>O<sub>2</sub>, Reprinted with permission from [<a href="#B87-biosensors-12-01183" class="html-bibr">87</a>]. (<b>C</b>) Schematic of Sb<sub>2</sub>O<sub>4</sub> nanoflowers grown on RGO and EC detection of NO. Reprinted with permission from [<a href="#B91-biosensors-12-01183" class="html-bibr">91</a>].</p> Full article ">Figure 4
<p>(<b>A</b>) Drawing of Co-NGA and Co-NGA/GCE used to detect DA secreted by live cells. Reprinted with permission from [<a href="#B108-biosensors-12-01183" class="html-bibr">108</a>]. (<b>B</b>) Synthesis of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>-RGO NC and its EC response for ST detection using GCE. Reprinted with permission from [<a href="#B125-biosensors-12-01183" class="html-bibr">125</a>]. (<b>C</b>) Schematic of ACC-RGO@Cu(BTC)@α-MnO<sub>2</sub> based flexible electrode prepared by anodic-induced electrodeposition and EC detection of Ach from biological matrices. Reprinted with permission from [<a href="#B127-biosensors-12-01183" class="html-bibr">127</a>]. (<b>D</b>) Depiction of Ach detection mechanism on the Surface of ITO-Au NPs-GO modified electrode. Reprinted with permission from [<a href="#B129-biosensors-12-01183" class="html-bibr">129</a>].</p> Full article ">Figure 5
<p>(<b>A</b>) Assembly technique of the dual-response MIP detection membrane for A and DA, (<b>a</b>,<b>b</b>) A and DA EC detection mechanisms. Reprinted with permission from [<a href="#B167-biosensors-12-01183" class="html-bibr">167</a>]. (<b>B</b>) Synthesis of Au/Pd−PPy/GR/GCE and EC response for A, G, T, C detection. Reprinted with permission from [<a href="#B169-biosensors-12-01183" class="html-bibr">169</a>]. (<b>C</b>) Fabrication of PtNPs/CB/RGO nanohybrid and EIS response for G sensing. Reprinted with permission from [<a href="#B171-biosensors-12-01183" class="html-bibr">171</a>]. (<b>D</b>) Pictorial presentation of GR/GC fabrication process (<b>a</b>), interface between different DNA bases (A, G, T, and C) and their oxidation reaction (<b>b</b>). Reprinted with permission from [<a href="#B172-biosensors-12-01183" class="html-bibr">172</a>].</p> Full article ">Figure 6
<p>(<b>A</b>) The schematic of the Ag/Ab/TMSPA/GO/GCE fabrication process in steps. Reprinted with permission from [<a href="#B181-biosensors-12-01183" class="html-bibr">181</a>]. (<b>B</b>) A flowchart showing the steps needed to make SARS-CoV-2 antigen and antibody immunosensors. Reprinted with permission from [<a href="#B185-biosensors-12-01183" class="html-bibr">185</a>]. (<b>C</b>) Depiction of EC immunosensing platform made from in situ functionalizations of AuNPs/RSG. Reprinted with permission from [<a href="#B189-biosensors-12-01183" class="html-bibr">189</a>].</p> Full article ">Scheme 1
<p>Representation of GR-based diverse range of bioanalytes detection using EC methods discussed in this review.</p> Full article ">
34 pages, 13006 KiB  
Article
Feature-Based Information Retrieval of Multimodal Biosignals with a Self-Similarity Matrix: Focus on Automatic Segmentation
by João Rodrigues, Hui Liu, Duarte Folgado, David Belo, Tanja Schultz and Hugo Gamboa
Biosensors 2022, 12(12), 1182; https://doi.org/10.3390/bios12121182 - 19 Dec 2022
Cited by 30 | Viewed by 3466
Abstract
Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, [...] Read more.
Biosignal-based technology has been increasingly available in our daily life, being a critical information source. Wearable biosensors have been widely applied in, among others, biometrics, sports, health care, rehabilitation assistance, and edutainment. Continuous data collection from biodevices provides a valuable volume of information, which needs to be curated and prepared before serving machine learning applications. One of the universal preparation steps is data segmentation and labelling/annotation. This work proposes a practical and manageable way to automatically segment and label single-channel or multimodal biosignal data using a self-similarity matrix (SSM) computed with signals’ feature-based representation. Applied to public biosignal datasets and a benchmark for change point detection, the proposed approach delivered lucid visual support in interpreting the biosignals with the SSM while performing accurate automatic segmentation of biosignals with the help of the novelty function and associating the segments grounded on their similarity measures with the similarity profiles. The proposed method performed superior to other algorithms in most cases of a series of automatic biosignal segmentation tasks; of equal appeal is that it provides an intuitive visualization for information retrieval of multimodal biosignals. Full article
(This article belongs to the Special Issue Advances in Biometrics and Biosensors Technologies and Applications)
►▼ Show Figures

Figure 1

Figure 1
<p>A visual structural description of functions on a time series for retrieving relevant events, segmenting, and associating the previously segmented subsequences based on the feature-based SSM. (<b>Left</b>): an Arterial Blood Pressure (ABP) signal’s SSM representing the pairwise similarity between subsequences, where the “novelty search” signal in green below the matrix demonstrates the novelty function and the “periodic search” signal in orange depicts the similarity function; (<b>right</b>): the clustering procedure of the novelty function-based segmented subsequences according to their similarity values in the SSM.</p> Full article ">Figure 2
<p>Event search in different ranks of dimensionality, timescales, and representation.</p> Full article ">Figure 3
<p>A step-by-step flowchart for calculating and analysing the SSM. The signal-based calculation requires input parameters of the window size <span class="html-italic">w</span> and the overlapping percentage <span class="html-italic">o</span> to fulfil the first-stage feature extraction. Features are extracted on each subsequence <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>s</mi> <msub> <mi>T</mi> <mn>1</mn> </msub> <mo>,</mo> <mi>s</mi> <msub> <mi>T</mi> <mn>2</mn> </msub> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mi>s</mi> <msub> <mi>T</mi> <mi>N</mi> </msub> <mo>)</mo> </mrow> </semantics></math>, where <span class="html-italic">N</span> is the total number of windows. <span class="html-italic">K</span> features are extracted from window <span class="html-italic">i</span> (<math display="inline"><semantics> <mrow> <mi>s</mi> <msub> <mi>T</mi> <mi>i</mi> </msub> </mrow> </semantics></math>: <math display="inline"><semantics> <mrow> <msub> <mi>f</mi> <msub> <mi>i</mi> <mn>1</mn> </msub> </msub> <mo>,</mo> <msub> <mi>f</mi> <msub> <mi>i</mi> <mn>2</mn> </msub> </msub> <mo>,</mo> <mo>…</mo> <mo>,</mo> <msub> <mi>f</mi> <msub> <mi>i</mi> <mi>K</mi> </msub> </msub> </mrow> </semantics></math>). Different features are associated with different shapes (<math display="inline"><semantics> <mrow> <mo>◯</mo> <mo>,</mo> <mo>□</mo> <mo>,</mo> <mo>⋄</mo> </mrow> </semantics></math> and Δ) in the figures. The features can be extracted on an <span class="html-italic">M</span>-variable record and each feature is positioned as a row on the <math display="inline"><semantics> <msub> <mi>F</mi> <mi>M</mi> </msub> </semantics></math> for the SSM computation.</p> Full article ">Figure 4
<p>The informative structures of an ABP signal’s SSM. The three main structures are highlighted in the simplified illustration: A—the homogeneous segments corresponding to periods in the ABP signal; B—the homogeneous segment representing missing data; C—the homogeneous segment cueing sensor detachment. The “blocks” in the figure accentuate homogeneous behaviour, while the paths in the figure depict periodicity in the segment. Segment C has a cross pattern, which symbolizes periodicity and symmetry. <math display="inline"><semantics> <mrow> <mi>n</mi> <mi>f</mi> </mrow> </semantics></math>: novelty function; <math display="inline"><semantics> <mrow> <mi>s</mi> <mi>f</mi> </mrow> </semantics></math>: similarity function; Δ: change points separating blocks A, B and C.</p> Full article ">Figure 5
<p>(<b>Left</b>): description of the matrix (kernel) used to compute the <span class="html-italic">novelty function</span>, based on the works of <span class="html-italic">Mueller</span> et al. [<a href="#B2-biosensors-12-01182" class="html-bibr">2</a>,<a href="#B73-biosensors-12-01182" class="html-bibr">73</a>]. The chequerboard pattern of the kernel <math display="inline"><semantics> <msub> <mi>K</mi> <mi>N</mi> </msub> </semantics></math> is achieved by combining the kernel <math display="inline"><semantics> <msub> <mi>K</mi> <mi>H</mi> </msub> </semantics></math> (homogeneity measure) and <math display="inline"><semantics> <msub> <mi>K</mi> <mi>C</mi> </msub> </semantics></math> (cross-similarity measure). Combined with a Gaussian function, the <math display="inline"><semantics> <msub> <mi>K</mi> <mi>G</mi> </msub> </semantics></math> is obtained; (<b>right</b>): the process to compute the novelty function based on the works of [<a href="#B2-biosensors-12-01182" class="html-bibr">2</a>,<a href="#B73-biosensors-12-01182" class="html-bibr">73</a>,<a href="#B94-biosensors-12-01182" class="html-bibr">94</a>]. Kernel <math display="inline"><semantics> <msub> <mi>K</mi> <mi>G</mi> </msub> </semantics></math> slides along the diagonal of the SSM to compute the <span class="html-italic">novelty function</span> presented as the bottom sub-plot. Positions A and B point to the effect of block transitions on the <span class="html-italic">novelty function</span>.</p> Full article ">Figure 6
<p>An SSM-based novelty search strategy to detect segmentation events on a signal piece from Dataset 1 (HAR, see <a href="#sec3dot1-biosensors-12-01182" class="html-sec">Section 3.1</a>). (<b>Top</b>): <math display="inline"><semantics> <mrow> <mi>w</mi> <mi>i</mi> <mi>n</mi> <mi>d</mi> <mi>o</mi> <msub> <mi>w</mi> <mrow> <mi>s</mi> <mi>i</mi> <mi>z</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> = 250 samples, <math display="inline"><semantics> <mrow> <mi>k</mi> <mi>e</mi> <mi>r</mi> <mi>n</mi> <mi>e</mi> <msub> <mi>l</mi> <mrow> <mi>s</mi> <mi>i</mi> <mi>z</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> = 45 samples, and overlap = 95% on the activity sequence <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>t</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mi>i</mi> <mi>n</mi> <mi>g</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>L</mi> <mi>a</mi> <mi>y</mi> <mi>i</mi> <mi>n</mi> <mi>g</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>W</mi> <mi>a</mi> <mi>l</mi> <mi>k</mi> <mi>i</mi> <mi>n</mi> <mi>g</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>U</mi> <mi>p</mi> <mi>s</mi> <mi>t</mi> <mi>a</mi> <mi>i</mi> <mi>r</mi> <mi>s</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>D</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> <mi>s</mi> <mi>t</mi> <mi>a</mi> <mi>i</mi> <mi>r</mi> <mi>s</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>S</mi> <mi>i</mi> <mi>t</mi> <mi>t</mi> <mi>i</mi> <mi>n</mi> <mi>g</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>S</mi> <mi>t</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mi>i</mi> <mi>n</mi> <mi>g</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>L</mi> <mi>a</mi> <mi>y</mi> <mi>i</mi> <mi>n</mi> <mi>g</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>W</mi> <mi>a</mi> <mi>l</mi> <mi>k</mi> <mi>i</mi> <mi>n</mi> <mi>g</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>U</mi> <mi>p</mi> <mi>s</mi> <mi>t</mi> <mi>a</mi> <mi>i</mi> <mi>r</mi> <mi>s</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>D</mi> <mi>o</mi> <mi>w</mi> <mi>n</mi> <mi>s</mi> <mi>t</mi> <mi>a</mi> <mi>i</mi> <mi>r</mi> <mi>s</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>S</mi> <mi>i</mi> <mi>t</mi> <mi>t</mi> <mi>i</mi> <mi>n</mi> <mi>g</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>S</mi> <mi>t</mi> <mi>a</mi> <mi>n</mi> <mi>d</mi> <mi>i</mi> <mi>n</mi> <mi>g</mi> <mover> <mo>→</mo> <mrow/> </mover> <mi>L</mi> <mi>a</mi> <mi>y</mi> <mi>i</mi> <mi>n</mi> <mi>g</mi> </mrow> </semantics></math>. The novelty function is presented and <b>peaks</b> are aligned with ground truth events, represented as the dashed line and circles; (<b>bottom left</b>): signal change point detection on segment <span class="html-italic">A</span> with a size of 5000 samples, an overlap of 75%, and a kernel size of 25 samples. The novelty function is displayed and <b>peaks</b> are aligned with ground truth events, represented as the dashed line and circles; (<b>bottom right</b>): further zooming in with a window size of 10 samples and an overlap of 95%, to reveal more periodic details of segment <span class="html-italic">B</span>. The similarity function is presented and <b>valleys</b> are aligned with ground truth events, represented as the dashed line and circles.</p> Full article ">Figure 7
<p>Novelty and similarity search on an ABP signal from Dataset 6 (BVP, see <a href="#sec3dot6-biosensors-12-01182" class="html-sec">Section 3.6</a>). (<b>Top</b>): a window size of 5000 samples, an overlap of 95%, and a kernel size of 200 samples. The trapezoidal and the square wave mark the ground truth of slow and fast postural transitions. Similarity profiles <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>A</mi> </msub> <mo>−</mo> <msub> <mi>P</mi> <mi>D</mi> </msub> </mrow> </semantics></math> show how similar each segment (<math display="inline"><semantics> <mrow> <mi>A</mi> <mo>−</mo> <mi>D</mi> </mrow> </semantics></math>) are. For instance, <math display="inline"><semantics> <msub> <mi>P</mi> <mi>D</mi> </msub> </semantics></math> is more similar to <math display="inline"><semantics> <msub> <mi>P</mi> <mi>B</mi> </msub> </semantics></math>. (<b>Bottom</b>): the first 10,000 samples, with a window size of 250 samples, an overlap of 95% and a kernel size of 200 samples. The right parts of the top and bottom subfigures plot the corresponding similarity profiles for each subsequence segmented by the novelty function. In both figures, the novelty function is displayed and <b>peaks</b> are aligned with ground truth events, represented as dashed lines and circles. The bottom plot also shows the similarity function (<span class="html-italic">sf</span>) with circles representing the ground truth of periods. In addition, similarity profiles <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>A</mi> </msub> <mo>−</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> </mrow> </semantics></math>, show how similar are each segment resulting from the novelty function. For instance, segment B is more similar to segment D, and this is verified by <math display="inline"><semantics> <msub> <mi>P</mi> <mi>D</mi> </msub> </semantics></math> being more similar to <math display="inline"><semantics> <msub> <mi>P</mi> <mi>B</mi> </msub> </semantics></math>.</p> Full article ">Figure 7 Cont.
<p>Novelty and similarity search on an ABP signal from Dataset 6 (BVP, see <a href="#sec3dot6-biosensors-12-01182" class="html-sec">Section 3.6</a>). (<b>Top</b>): a window size of 5000 samples, an overlap of 95%, and a kernel size of 200 samples. The trapezoidal and the square wave mark the ground truth of slow and fast postural transitions. Similarity profiles <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>A</mi> </msub> <mo>−</mo> <msub> <mi>P</mi> <mi>D</mi> </msub> </mrow> </semantics></math> show how similar each segment (<math display="inline"><semantics> <mrow> <mi>A</mi> <mo>−</mo> <mi>D</mi> </mrow> </semantics></math>) are. For instance, <math display="inline"><semantics> <msub> <mi>P</mi> <mi>D</mi> </msub> </semantics></math> is more similar to <math display="inline"><semantics> <msub> <mi>P</mi> <mi>B</mi> </msub> </semantics></math>. (<b>Bottom</b>): the first 10,000 samples, with a window size of 250 samples, an overlap of 95% and a kernel size of 200 samples. The right parts of the top and bottom subfigures plot the corresponding similarity profiles for each subsequence segmented by the novelty function. In both figures, the novelty function is displayed and <b>peaks</b> are aligned with ground truth events, represented as dashed lines and circles. The bottom plot also shows the similarity function (<span class="html-italic">sf</span>) with circles representing the ground truth of periods. In addition, similarity profiles <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mi>A</mi> </msub> <mo>−</mo> <msub> <mi>P</mi> <mi>G</mi> </msub> </mrow> </semantics></math>, show how similar are each segment resulting from the novelty function. For instance, segment B is more similar to segment D, and this is verified by <math display="inline"><semantics> <msub> <mi>P</mi> <mi>D</mi> </msub> </semantics></math> being more similar to <math display="inline"><semantics> <msub> <mi>P</mi> <mi>B</mi> </msub> </semantics></math>.</p> Full article ">Figure 8
<p>An ECG signal with a <span class="html-italic">pulsus paradoxus</span> condition starting at the 10,000th sample from Dataset 7 (ECG Pulsus Paradoxus, see <a href="#sec3dot7-biosensors-12-01182" class="html-sec">Section 3.7</a>). (<b>Left</b>): the SSM diagnoses two modes in the signal, whose patterns are zoomed in the circle thumbnails, respectively; (<b>right</b>): zooming parts of the original signal can verify SSM’s ability of automatic ECG pattern change detection and contribution to segmentation. The novelty function is presented, and the peak is aligned with the ground truth event, represented as a circle.</p> Full article ">Figure 9
<p>The proposed method was applied to the <span class="html-italic">Occupancy</span> record of Dataset 5 (CPDBenchmark, see <a href="#sec3dot5-biosensors-12-01182" class="html-sec">Section 3.5</a>). (<b>Left</b>): calculations on the separate <math display="inline"><semantics> <mrow> <mi>C</mi> <msub> <mi>O</mi> <mn>2</mn> </msub> </mrow> </semantics></math> time series only; (<b>right</b>): calculations performed by extracting features on the complete four time series. The novelty function is presented and <span class="html-italic">peaks</span> are aligned with ground truth events, represented as the dashed line and circles.</p> Full article ">Figure 10
<p>Critical distance diagram comparing the methods used in [<a href="#B8-biosensors-12-01182" class="html-bibr">8</a>] (except <span class="html-italic">RBOCPDMS</span>) and the <span class="html-italic">novelty function</span> on Dataset 5 (CPDBenchmark, see <a href="#sec3dot5-biosensors-12-01182" class="html-sec">Section 3.5</a>). The performance measure corresponds to the F1-score for all single-dimension datasets of the benchmark, except for the ones identified in <a href="#biosensors-12-01182-t003" class="html-table">Table 3</a> with a grey background. A thick horizontal line groups a set of classifiers that are not significantly different in the statistical test [<a href="#B96-biosensors-12-01182" class="html-bibr">96</a>].</p> Full article ">Figure 11
<p>An illustrative example of window length intuition on records of Dataset 2 (ECG1, see <a href="#sec3dot2-biosensors-12-01182" class="html-sec">Section 3.2</a>). Top: different SSMs on the same ECG record A computed with sequentially larger window lengths from 0.01 to 2 s. The novelty functions are calculated with a kernel size equal to the window size and an overlap of 95%. Bottom: The 1-second window length is further applied as an example to indicate that parameters turned in the representation experiments can be generalized to all other records of the same dataset (B-I) to compute their corresponding SSM representations and novelty functions.</p> Full article ">Figure A1
<p>F1-scores’ distribution for three segmentation methods on Datasets 1–4. WS: window-based segmentation; BS: binary segmentation; Novelty: the proposed novelty function-based segmentation.</p> Full article ">
24 pages, 2069 KiB  
Review
Recent Development of Fluorescent Nanodiamonds for Optical Biosensing and Disease Diagnosis
by Shahzad Ahmad Qureshi, Wesley Wei-Wen Hsiao, Lal Hussain, Haroon Aman, Trong-Nghia Le and Muhammad Rafique
Biosensors 2022, 12(12), 1181; https://doi.org/10.3390/bios12121181 - 19 Dec 2022
Cited by 27 | Viewed by 4678
Abstract
The ability to precisely monitor the intracellular temperature directly contributes to the essential understanding of biological metabolism, intracellular signaling, thermogenesis, and respiration. The intracellular heat generation and its measurement can also assist in the prediction of the pathogenesis of chronic diseases. However, intracellular [...] Read more.
The ability to precisely monitor the intracellular temperature directly contributes to the essential understanding of biological metabolism, intracellular signaling, thermogenesis, and respiration. The intracellular heat generation and its measurement can also assist in the prediction of the pathogenesis of chronic diseases. However, intracellular thermometry without altering the biochemical reactions and cellular membrane damage is challenging, requiring appropriately biocompatible, nontoxic, and efficient biosensors. Bright, photostable, and functionalized fluorescent nanodiamonds (FNDs) have emerged as excellent probes for intracellular thermometry and magnetometry with the spatial resolution on a nanometer scale. The temperature and magnetic field-dependent luminescence of naturally occurring defects in diamonds are key to high-sensitivity biosensing applications. Alterations in the surface chemistry of FNDs and conjugation with polymer, metallic, and magnetic nanoparticles have opened vast possibilities for drug delivery, diagnosis, nanomedicine, and magnetic hyperthermia. This study covers some recently reported research focusing on intracellular thermometry, magnetic sensing, and emerging applications of artificial intelligence (AI) in biomedical imaging. We extend the application of FNDs as biosensors toward disease diagnosis by using intracellular, stationary, and time-dependent information. Furthermore, the potential of machine learning (ML) and AI algorithms for developing biosensors can revolutionize any future outbreak. Full article
(This article belongs to the Special Issue Fluorescent Materials with Excellent Biocompatibility and Their Application in Bio-Sensing, Bio-Imaging)
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<p>Optical spectroscopy of NV center. (<b>a</b>) The structure of the NV center in a diamond comprising a nitrogen atom connected to a vacancy (center). The NV center is optically excited typically with 590–532 nm lasers, whereas the emission lies within 500–800 nm. (<b>b</b>) PL spectra of a single NV center displaying ZPL (638 nm) and PSB; inset shows the temperature-dependent shift in ZPL. (<b>c</b>) The mathematical representation of the shape of ZPL using a Lorentzian function with an exponential background.</p> Full article ">Figure 2
<p>Electronic structure of NV center and magnetic interaction. (<b>a</b>) Schematic of the energy levels of NV center, depicting optical excitation and relaxation. Intersystem crossing through singlets states (<sup>1</sup><span class="html-italic">A</span>,<sup>1</sup><span class="html-italic">E</span>) is shown in black arrows and the resonant microwave excitation is purple. (<b>b</b>) The ODMR spectra at zero field (<span class="html-italic">B</span> = 0). (<b>c</b>) The ODMR spectra at (<span class="html-italic">B</span> ≠ 0) displaying the Zeeman splitting between the spin sublevels. (<b>d</b>) Thermally induced shift in the position of <span class="html-italic">D<sub>gs</sub></span>.</p> Full article ">Figure 3
<p>Wide-field magnetic imaging with FNDs. (<b>a</b>) Optical spin microscope based on the luminescence of NV centers in FNDs for bioimaging. FNDs are spin coated on a glass coverslip and printed with gold electrodes for microwave signal transmission. The microscope comprises objective, dichroic mirror (DM), filter, and permanent magnet. Fluorescence images are recorded by EMCCD while the sample is excited with a 532 nm laser and a scanning microwave frequency source. (<b>b</b>) Schematic of surface-modified FNDs for bio-labeling and tracking. (<b>c</b>) The reconstructed magnetic image displaying the presence of labeled magnetic organisms.</p> Full article ">Figure 4
<p>Illustration of the multispectral superresolution microscopy using NV centers. (<b>a</b>) FNDs shown as arbitrarily dispersed bright spots. (<b>b</b>) Single FND illustrated in large view. (<b>c</b>) The ODMR spectra of single FND, as in (<b>b</b>), displaying two differently orientated NV centers. (<b>d</b>) The reconstructed superresolution image indicating red and blue spots as two different NV centers.</p> Full article ">Figure 5
<p>Schematic of SARS-CoV-2 detection using FND [<a href="#B75-biosensors-12-01181" class="html-bibr">75</a>]. (<b>a</b>) Surface-modified FNDs which are coated with PEI are subject to binding with c-DNA-Gd<sup>3+</sup> complexes. When these modified FNDs are exposed to virus RNA, the c-DNA-Gd<sup>3+</sup> molecules covalently bond with the virus RNA and detach from the surface of the FND, resulting in a lower magnetic noise sensed by the NV center. The change in magnetic noise sensed by NV center can be observed in terms of NV luminescence emission. (<b>b</b>) The sequence of optical metrology using FNDs for T<sub>1</sub> relaxometry.</p> Full article ">Figure 6
<p>Schematic of the principle of HIV-1 RNA detection using FNDs and RT-RPA on the LFA. Labeled amplicons were generated during the reaction between Digoxigenin (DIG) and biotin-modified primers. The DNA primer binds with the HIV-1 virus RNA which is extracted from the patient. The primer then binds with c-DNA followed by purification and amplification. The labeled amplicons specifically bind with the functionalized nanodiamonds (FND-anti-DIG)) and streptavidin (purple) being immobilized on the lateral flow test strip. The NV center luminescence is recorded by applying microwave (MW) pulsed sequences in ON and OFF states.</p> Full article ">Figure 7
<p>Illustration of AI scheme for the assessment and prediction of a biological process (BP). The proposed model relies upon the signal acquired from nanodiamonds which act as sensors providing electrical signals and an interface between BP and the data acquisition process. The accuracy of this model relies upon the comparison of the goodness of fit from the experimental and predicted curve based on root mean squared error values.</p> Full article ">
12 pages, 9174 KiB  
Article
A Simple Label-Free Aptamer-Based Electrochemical Biosensor for the Sensitive Detection of C-Reactive Proteins
by Huilin Gao, Yongchang Bai, Baixun He and Cherie S. Tan
Biosensors 2022, 12(12), 1180; https://doi.org/10.3390/bios12121180 - 18 Dec 2022
Cited by 10 | Viewed by 3274
Abstract
The level of C-reactive protein (CRP) in the human body is closely associated with cardiovascular diseases and inflammation. In this study, a label-free functionalized aptamer sensor was attached to an electrode trimmed with in-gold nanoparticles and carboxylated graphene oxide (AuNPs/GO-COOH) to achieve sensitive [...] Read more.
The level of C-reactive protein (CRP) in the human body is closely associated with cardiovascular diseases and inflammation. In this study, a label-free functionalized aptamer sensor was attached to an electrode trimmed with in-gold nanoparticles and carboxylated graphene oxide (AuNPs/GO-COOH) to achieve sensitive measurements relative to CRP. Gold nanoparticles were selected for this study due to super stability, remarkably high electrical conductivity, and biocompatibility. In addition, carboxylated graphene oxide was utilized to promote the anchorage of inducer molecules and to increase detection accuracies. The sensing signal was recorded using differential pulse voltammetry (DPV), and it produced a conspicuous peak current obtained at approximately −0.4 V. Furthermore, the adapted sensor manifested a broad linear span from 0.001 ng/mL to 100 ng/mL. The results also demonstrated that this aptamer sensor had superior stability, specificity, and reproducibility. This aptamer-based electrochemical sensor has enormous potential in complex application situations with interfering substances. Full article
(This article belongs to the Special Issue Advanced Label-Free Electrochemical Affinity Biosensors)
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<p>Illustration of the manufacturing process of the label-free C-reactive protein (CRP) aptamer sensor.</p> Full article ">Figure 2
<p>Characterization of the SPE/AuNPs/GO-COOH aptamer’s sensing interface. (<b>A</b>) Electrodeposition of AuNPs using the SWV method with five scans. (<b>B</b>) SEM electron micrograph of the bare electrode. (<b>C</b>) SEM characterization of the electrode surface after the electrodeposition of AuNPs. (<b>D</b>) Surface characterization of the GO-COOH physically adsorbed on an electrode containing AuNPs.</p> Full article ">Figure 3
<p>(<b>A</b>) Voltage-current diagram of CRP solutions from 0.001 ng/mL to 100 ng/mL using the DPV method. (<b>B</b>) The curve of peak currents for each concentration of CRP fitted to the difference between the 0 ng/mL CRP solution and the logarithm of the CRP concentration (n = 3).</p> Full article ">Figure 4
<p>Selectivity, stability, and reproducibility tests based on the SPE/AuNPs/GO-COOH aptamer sensor. (<b>A</b>) Voltage-current plot of CRP vs. interfering solutions using the DPV method. Red: 1 ng/mL CRP; black: PBS solution; blue: 1 ng/mL PPBP; green: 1 ng/mL BNP; orange: 1 ng/mL PSA; purple: 1 ng/mL CA125. (<b>B</b>) The plot of the selectivity error analysis corresponds to (<b>A</b>) (n = 3). (<b>C</b>) Error analysis plots were obtained by selecting the electrodes on day 1, day 3, day 5, day 7, and day 9 for testing against 1 ng/mL CRP solution (n = 3). (<b>D</b>) Reproducibility test plots of five aptamer-sensing electrodes for multiple assays of 0.001 ng/mL CRP (n = 3).</p> Full article ">
14 pages, 3323 KiB  
Article
A Novel Detachable, Reusable, and Versatile Acoustic Tweezer Manipulation Platform for Biochemical Analysis and Detection Systems
by Yukai Liu, Miaomiao Ji, Yichi Zhang, Xiaojun Qiao, Nanxin Yu, Chenxi Ding, Lingxiao Yang, Rui Feng, Xiujian Chou and Wenping Geng
Biosensors 2022, 12(12), 1179; https://doi.org/10.3390/bios12121179 - 18 Dec 2022
Cited by 4 | Viewed by 2214
Abstract
Multifunctional, integrated, and reusable operating platforms are highly sought after in biochemical analysis and detection systems. In this study, we demonstrated a novel detachable, reusable acoustic tweezer manipulation platform that is flexible and versatile. The free interchangeability of different detachable microchannel devices on [...] Read more.
Multifunctional, integrated, and reusable operating platforms are highly sought after in biochemical analysis and detection systems. In this study, we demonstrated a novel detachable, reusable acoustic tweezer manipulation platform that is flexible and versatile. The free interchangeability of different detachable microchannel devices on the acoustic tweezer platform was achieved by adding a waveguide layer (glass) and a coupling layer (polydimethylsiloxane (PDMS) polymer film). We designed and demonstrated the detachable multifunctional acoustic tweezer platform with three cell manipulation capabilities. In Demo I, the detachable acoustic tweezer platform is demonstrated to have the capability for parallel processing and enrichment of the sample. In Demo II, the detachable acoustic tweezer platform with capability for precise cell alignment is demonstrated. In Demo III, it was demonstrated that the detachable acoustic tweezer platform has the capability for the separation and purification of cells. Through experiments, our acoustic tweezer platform has good acoustic retention ability, reusability, and stability. More capabilities can be expanded in the future. It provides a simple, economical, and multifunctional reusable operating platform solution for biochemical analysis and detection systems. Full article
(This article belongs to the Special Issue Nano/Micro Technologies for Biosensors: A Themed Issue in Honor of Prof. Dr. Chung-Chiun Liu)
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<p>Detachable acoustic tweezer manipulation platform: (<b>a</b>) working diagram of the platform, (<b>b</b>) photograph of the platform, (<b>c</b>) device composition on the cross-section and SSAW radiation schematic, and (<b>d</b>) pressure nodes and force analysis.</p> Full article ">Figure 2
<p>Three device models used for acoustic transmission performance testing.</p> Full article ">Figure 3
<p>The frequency response of the SAW device: (<b>a</b>) the measured return loss (S11) verifies the excitation frequency (30.25 MHz) activated by IDT<sub>1</sub> and IDT<sub>2</sub>, and (<b>b</b>) the measured insertion loss (S21) characterizes the effect of different coupling layers on the acoustic transmission performance.</p> Full article ">Figure 4
<p>Flow chart of the chip manufacturing process: (<b>a</b>) IDT preparation process diagram and (<b>b</b>) fabrication schematic of the detachable microchannels.</p> Full article ">Figure 5
<p>(<b>a</b>) Numerical simulation of the wave propagation and radiation on a cross-section of the detachable acoustic tweezer manipulation platform. (<b>bi</b>) Schematic diagram of the particle arrangement. (<b>bii</b>) Distribution of acoustic pressure. (<b>biii</b>,<b>biv</b>) Patterning of 700 nm particles before and after SSAW turn-on. (<b>ci</b>,<b>cii</b>) The aggregation behavior of fluorescent particles observed under the fluorescence field. (<b>ciii</b>) The distribution of standing wave amplitude in one cycle. (<b>civ</b>) Normalized fluorescence intensity diagram of the arrangement of particles.</p> Full article ">Figure 6
<p>(<b>a</b>) Schematic diagram of the particle precision alignment based on the detachable acoustic tweezer platform. (<b>bi</b>,<b>bii</b>) Force and alignment of the particles in the channel when the pressure line is in the center. (<b>biii</b>,<b>biv</b>) Force and alignment of particles in the channel when the sound pressure line is on both sides.</p> Full article ">Figure 7
<p>Test results of the device separation performance: (<b>a</b>) Schematic diagram of particle separation based on the detachable acoustic tweezer platform. (<b>bi</b>) Time-lapse superimposed image of the particle separation process. (<b>bii</b>) Performance characterization of the stability and the reusability of the device.</p> Full article ">Figure 8
<p>Experimentation on the actual application capability of the manipulation platform: (<b>a</b>) the aggregation behavior of RBCs before and after the acoustic field is turned on, (<b>b</b>) the precise cell alignment of RBCs, and (<b>c</b>) the separation of 20 μm PS particles and red blood cells.</p> Full article ">
14 pages, 2683 KiB  
Article
Monitoring and Regulating Intracellular GPX4 mRNA Using Gold Nanoflare Probes and Enhancing Erastin-Induced Ferroptosis
by Xiaoyan Liu, Qiangqiang Yang, Yanan Sui, Qiaoli Yue, Shuqing Yan, Chuan Li and Min Hong
Biosensors 2022, 12(12), 1178; https://doi.org/10.3390/bios12121178 - 17 Dec 2022
Cited by 1 | Viewed by 2405
Abstract
Glutathione peroxidase 4 (GPX4) plays an important effect on ferroptosis. Down-regulating the expression of GPX4 mRNA can decrease the content of GPX4. In this work, a gold nanoflare (AuNF) probe loaded with anti-sense sequences targeting for GPX4 mRNA was designed to monitor and [...] Read more.
Glutathione peroxidase 4 (GPX4) plays an important effect on ferroptosis. Down-regulating the expression of GPX4 mRNA can decrease the content of GPX4. In this work, a gold nanoflare (AuNF) probe loaded with anti-sense sequences targeting for GPX4 mRNA was designed to monitor and down-regulate intracellular GPX4 mRNA using fluorescence imaging in situ and using anti-sense technology. The results revealed that there was a marked difference for the expression of GPX4 mRNA in different cell lines, and the survival rate of cancer cells was not significantly effected when the relative mRNA and protein expression levels of GPX4 was down-regulated by AuNF probes. However, when co-treated with AuNF probes, the low expression of GPX4 strengthened erastin-induced ferroptosis, and this synergy showed a better effect on inhibiting the proliferation of cancer cells. Full article
(This article belongs to the Special Issue Biosensing and Diagnosis)
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<p>Schematic illustration of designation of AuNF probes, mechanism of down-regulating GPX4, and enhancement of erastin-induced ferroptosis for cancer cells.</p> Full article ">Figure 2
<p>TEM images of AuNPs (<b>A</b>), UV-vis characterization of AuNPs and AuNF probes (<b>B</b>), hydrodynamic sizes of AuNPs (<b>C</b>) and AuNF probes (<b>D</b>) determined by DLS.</p> Full article ">Figure 3
<p>Fluorescence spectra of AuNF probes incubated with different concentrations of Target-DNA (0, 2.5, 5, 10, 50, 100, 200, and 300 nM) (<b>A</b>) or HeLa cell lysis solution (<b>B</b>).</p> Full article ">Figure 4
<p>Fluorescence images of different cancer cell lines (<b>A</b>) and normal cell lines (<b>B</b>) that were treated with AuNF probes (1.5 nM) for 4 h. Flow cytometric analysis of different cell lines (<b>C</b>) collected after the CLSM determination.</p> Full article ">Figure 5
<p>(<b>A</b>) qRT-PCR analysis of GPX4 mRNA in HeLa cells after being treated with AuNF probes (1.5 nM) or AuNF probe analogs (1.5 nM) for different times (12, 24, 36, and 48 h). (<b>B</b>) Immunofluorescence confocal imaging of GPX4 expression (purple) in HeLa cells after being treated with AuNF probes (1.5 nM) or PBS (Blank) for 48 h. (<b>C</b>) The activity of Se-GPX in HeLa cells after being treated with different concentrations of AuNF probes (0, 0.5, 0.8, 1.2, and 1.5 nM) for 48 h. (<b>D</b>) The activity of Se-GPX in HeLa cells after being treated with AuNF probes (1.5 nM) for different times (0, 12, 24, 36, and 48 h).</p> Full article ">Figure 6
<p>(<b>A</b>) Cell viabilities of different cancer cell lines when treated with different concentrations of erastin (0.1, 0.5, 1.0, 2.0, 5.0, 10.0, and 15.0 µM) for 48 h. Cell viability of HeLa (<b>B</b>), A549 (<b>C</b>), HepG2 (<b>D</b>), MCF-7 (<b>E</b>), and MDA-MB-231 cells (<b>F</b>) when treated with AuNF probes (1.5 nM), erastin (2.0 µM), and AuNF probes (1.5 nM) + erastin (2.0 µM) for different times (12, 24, and 48 h). <span class="html-italic">p</span> &lt; 0.05 (*); <span class="html-italic">p</span> &lt; 0.01 (**).</p> Full article ">Figure 7
<p>(<b>A</b>) Confocal images of HeLa cells showing the ROS response when cells were treated by different systems. (<b>B</b>) Mean fluorescence intensity of confocal images of HeLa cells shown in (<b>A</b>).</p> Full article ">
11 pages, 2606 KiB  
Article
On-Chip Organoid Formation to Study CXCR4/CXCL-12 Chemokine Microenvironment Responses for Renal Cancer Drug Testing
by Adem Ozcelik, Burcin Irem Abas, Omer Erdogan, Evrim Cevik and Ozge Cevik
Biosensors 2022, 12(12), 1177; https://doi.org/10.3390/bios12121177 - 17 Dec 2022
Cited by 6 | Viewed by 2638
Abstract
Organoid models have gained importance in recent years in determining the toxic effects of drugs in cancer studies. Organoid designs with the same standardized size and cellular structures are desired for drug tests. The field of microfluidics offers numerous advantages to enable well-controlled [...] Read more.
Organoid models have gained importance in recent years in determining the toxic effects of drugs in cancer studies. Organoid designs with the same standardized size and cellular structures are desired for drug tests. The field of microfluidics offers numerous advantages to enable well-controlled and contamination-free biomedical research. In this study, simple and low-cost microfluidic devices were designed and fabricated to develop an organoid model for drug testing for renal cancers. Caki human renal cancer cells and mesenchymal stem cells isolated from human umbilical cord were placed into alginate hydrogels. The microfluidic system was implemented to form size-controllable organoids within alginate hydrogels. Alginate capsules of uniform sizes formed in the microfluidic system were kept in cell culture for 21 days, and their organoid development was studied with calcein staining. Cisplatin was used as a standard chemotherapeutic, and organoid sphere structures were examined as a function of time with an MTT assay. HIF-1α, CXCR4 and CXCL-12 chemokine protein, and CXCR4 and CXCL-12 gene levels were tested in organoids and cisplatin responses. In conclusion, it was found that the standard renal cancer organoids made on a lab-on-a-chip system can be used to measure drug effects and tumor microenvironment responses. Full article
(This article belongs to the Collection Recent Developments in Microfluidics)
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<p>Fabricated PMMA device. (<b>a</b>) Schematic of the microfluidic device. (<b>b</b>) Actual picture of the microfluidic device fabricated using PMMA. (<b>c</b>) Detail dimensions of the microfluidic device (drawn to scale).</p> Full article ">Figure 2
<p>Working principle of the microfluidic device. (<b>a</b>) Device outline and injected fluids are shown. (<b>b</b>) Generated alginate hydrogel droplets are shown. (<b>c</b>) Collected alginate beads are shown.</p> Full article ">Figure 3
<p>Characterization of organoid modelling developed from Caki and MSC cells and treatment by Cisplatin. Microscopic images showing growth of Caki and MSC cells and Caki-MSC co-culture cells on 3rd day (top row). Alginate beads organoid showing sphere formation, hematoxylin and eosin (H&amp;E) staining on 21st day (bottom row).</p> Full article ">Figure 4
<p>(<b>a</b>) HIF-1α protein levels of renal organoid cultured from day 1 to day 21 (* <span class="html-italic">p</span> ˂ 0.05, ** <span class="html-italic">p</span> ˂ 0.01 compared to Caki control cells). (<b>b</b>) Cell viability of cisplatin treatment with Caki cells at 24 h, 48 h, and 72 h. (<b>c</b>) Calcein staining of alginate renal organoid cultured from day 1 to day 21 (*** <span class="html-italic">p</span> ˂ 0.001 compared to Caki control cells, <sup>+++</sup> <span class="html-italic">p</span> ˂ 0.001 compare to alginate organoid without cisplatin for each day).</p> Full article ">Figure 5
<p>Tumor microenvironment response with/without Cisplatin in renal organoid modeling in alginate. (<b>a</b>) CXCR4 protein levels; (<b>b</b>) CXCR4 gene expression levels; (<b>c</b>) CXCL-12 protein expression levels; (<b>d</b>) CXCL-12 gene expression levels with/without Cisplatin in renal organoid modeling on the 1st, 7th, 14th, and 21st days (* <span class="html-italic">p</span> ˂ 0.05, ** <span class="html-italic">p</span> ˂ 0.01, *** <span class="html-italic">p</span> ˂ 0.001 compared to Caki control cells, <sup>+</sup> <span class="html-italic">p</span> ˂ 0.05, <sup>++</sup> <span class="html-italic">p</span> ˂ 0.01, and <sup>+++</sup> <span class="html-italic">p</span> ˂ 0.001 compared to alginate organoid without cisplatin for each day).</p> Full article ">
44 pages, 9519 KiB  
Review
Emerging Materials, Wearables, and Diagnostic Advancements in Therapeutic Treatment of Brain Diseases
by Brindha Ramasubramanian, Vundrala Sumedha Reddy, Vijila Chellappan and Seeram Ramakrishna
Biosensors 2022, 12(12), 1176; https://doi.org/10.3390/bios12121176 - 16 Dec 2022
Cited by 4 | Viewed by 5387
Abstract
Among the most critical health issues, brain illnesses, such as neurodegenerative conditions and tumors, lower quality of life and have a significant economic impact. Implantable technology and nano-drug carriers have enormous promise for cerebral brain activity sensing and regulated therapeutic application in the [...] Read more.
Among the most critical health issues, brain illnesses, such as neurodegenerative conditions and tumors, lower quality of life and have a significant economic impact. Implantable technology and nano-drug carriers have enormous promise for cerebral brain activity sensing and regulated therapeutic application in the treatment and detection of brain illnesses. Flexible materials are chosen for implantable devices because they help reduce biomechanical mismatch between the implanted device and brain tissue. Additionally, implanted biodegradable devices might lessen any autoimmune negative effects. The onerous subsequent operation for removing the implanted device is further lessened with biodegradability. This review expands on current developments in diagnostic technologies such as magnetic resonance imaging, computed tomography, mass spectroscopy, infrared spectroscopy, angiography, and electroencephalogram while providing an overview of prevalent brain diseases. As far as we are aware, there hasn’t been a single review article that addresses all the prevalent brain illnesses. The reviewer also looks into the prospects for the future and offers suggestions for the direction of future developments in the treatment of brain diseases. Full article
(This article belongs to the Special Issue Advances in Wearable Biosensors for Healthcare Monitoring)
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<p>Schematic overview of the review, where the common brain diseases namely, Autoimmune brain diseases, Epilepsy, Brain infections, Brain illness, Neurodegenerative, Neurodevelopmental disorders, brain tumour and strokes, followed by the advancements in the diagnostic tools and treatment approaches highlighting the use of 5G technology, Artificial Intelligence, nanotechnology, self-powered wearables, and microelectromechanical systems.</p> Full article ">Figure 2
<p>(<b>a</b>) Schematic of healthy neuron and brain immune cells (<b>b</b>) Microglial AND neuronal receptors in CNS (adapted with permission from [<a href="#B36-biosensors-12-01176" class="html-bibr">36</a>] Springer Nature, 2008) (<b>c</b>) Schematic comparison between healthy neural cells and diseased cells with inflammation (adapted with permission from [<a href="#B37-biosensors-12-01176" class="html-bibr">37</a>] Frontiers, 2015).</p> Full article ">Figure 3
<p>(<b>A</b>–<b>D</b>) Schematic illustration of brain morphology of male and female mice tested for Kcnq2<sup>T274M/+</sup> genotype (adapted with permission from [<a href="#B92-biosensors-12-01176" class="html-bibr">92</a>], Wiley, 2022).</p> Full article ">Figure 4
<p>Meningeal tissues or cells in CNS illness or damage (adapted with permission from [<a href="#B108-biosensors-12-01176" class="html-bibr">108</a>] Frontiers, 2021). (<b>A</b>) a CNS injury or illness. (<b>B</b>) fibrosis, which is brought on by enhanced perivascular cell growth and extracellular (ECM) accumulation. (<b>C</b>) the summary of the functional contributions made by immune cells situated in the meninges in various CNS pathological conditions. (<b>D</b>) the summary of the cellular alterations that have occurred in the meninges’ lymphoid system in response to various CNS diseases and traumas.</p> Full article ">Figure 5
<p>Schematic of significant cellular pathway disruptions causing Huntington’s illness (adapted with permission from [<a href="#B150-biosensors-12-01176" class="html-bibr">150</a>] Elsevier, 2013).</p> Full article ">Figure 6
<p>(<b>a</b>) Organizational framework for NDD pathogenesis and levels of analysis (adapted with permission from [<a href="#B172-biosensors-12-01176" class="html-bibr">172</a>] Elsevier, 2022) (<b>b</b>) Protective pathways activated by cerebral ischemia, brain stroke (adapted with permission from [<a href="#B173-biosensors-12-01176" class="html-bibr">173</a>] Springer Nature, 2011) (<b>c</b>) Mechanism of interaction of tumor cells with stromal cells in tumor microenvironment promoting tumor growth, invasion, and metastasis (adapted with permission from [<a href="#B174-biosensors-12-01176" class="html-bibr">174</a>] Springer, 2020).</p> Full article ">Figure 7
<p>(<b>a</b>) The construction of the EEG histogram and the age pyramid with sections for male and female patients using ML (adapted with permission from [<a href="#B197-biosensors-12-01176" class="html-bibr">197</a>] Elsevier, 2020) (<b>b</b>) The temporal electrode sites T3 and T4 exhibiting a pathological class with increase in amplitudes measured by neural networks (adapted with permission from [<a href="#B197-biosensors-12-01176" class="html-bibr">197</a>] Elsevier, 2020) (<b>c</b>) Mechanism of conductivity of the hybrid PDMS/CNT biocompatible composites and their resistance vs. time plots (adapted with permission from [<a href="#B198-biosensors-12-01176" class="html-bibr">198</a>], MDPI, 2021).</p> Full article ">Figure 8
<p>(<b>a</b>,<b>b</b>) in vivo PET spatial range of AV-1451 retention correlated with age, Aβ, and tau. (Adapted with permission from [<a href="#B233-biosensors-12-01176" class="html-bibr">233</a>], Cell Press, 2016).</p> Full article ">Figure 9
<p>(<b>a</b>) Transport mechanism of O<sub>2</sub>, CO<sub>2</sub> and lipophilic molecules across BBB (adapted with permission from [<a href="#B72-biosensors-12-01176" class="html-bibr">72</a>] Elsevier, 2020) (<b>b</b>) In vivo and ex vivo images of drug loaded liposome administrated mice after 1, 2, 6 and 24 h (adapted with permission from [<a href="#B5-biosensors-12-01176" class="html-bibr">5</a>], Elsevier, 2018).</p> Full article ">Figure 10
<p>(<b>a</b>) Schematic representation of the bioprinting process and the bioprinted mini-brain (<b>aA</b>) Preparation and two-step bioprinting of the mini-brains. (<b>aB</b>) Close-up and cross-sectional view of the bioprinted mini brains. (<b>aC</b>) Glioblastoma area in red. (<b>aD</b>) Cross-section in the frontal plain (<b>aE</b>) Schematic of interaction between glioblastoma cells and macrophages (adapted with permission from [<a href="#B351-biosensors-12-01176" class="html-bibr">351</a>] Wiley, 2019) (<b>b</b>) Components of engineered brain-mimetic platforms involving biophysical parameters, oxygen content, biochemical and cellular composition (adapted with permission from [<a href="#B352-biosensors-12-01176" class="html-bibr">352</a>] AIP, 2021).</p> Full article ">
17 pages, 3542 KiB  
Article
Graphene Oxide-Magnetic Nanoparticles Loaded Polystyrene-Polydopamine Electrospun Nanofibers Based Nanocomposites for Immunosensing Application of C-Reactive Protein
by Simge Ketmen, Simge Er Zeybekler, Sultan Sacide Gelen and Dilek Odaci
Biosensors 2022, 12(12), 1175; https://doi.org/10.3390/bios12121175 - 16 Dec 2022
Cited by 11 | Viewed by 3110
Abstract
The large surface area/volume ratio and controllable surface conformation of electrospun nanofibers (ENFs) make them highly attractive in applications where a large surface area is desired, such as sensors and affinity membranes. In this study, nanocomposite-based ENFs were produced and immobilization of Anti-CRP [...] Read more.
The large surface area/volume ratio and controllable surface conformation of electrospun nanofibers (ENFs) make them highly attractive in applications where a large surface area is desired, such as sensors and affinity membranes. In this study, nanocomposite-based ENFs were produced and immobilization of Anti-CRP was carried out for the non-invasive detection of C-reactive protein (CRP). Initially, the synthesis of graphene oxide (GO) was carried out and it was modified with magnetic nanoparticles (MNP, Fe3O4) and polydopamine (PDA). Catechol-containing and quinone-containing functional groups were created on the nanocomposite surface for the immobilization of Anti-CRP. Polystyrene (PS) solution was mixed with rGO-MNP-PDA nanocomposite and PS/rGO-MNP-PDA ENFs were produced with bead-free, smooth, and uniform. The surface of the screen-printed carbon electrode (SPCE) was covered with PS/rGO-MNP-PDA ENFs by using the electrospinning technique under the determined optimum conditions. Next, Anti-CRP immobilization was carried out and the biofunctional surface was created on the PS/rGO-MNP-PDA ENFs coated SPCE. Moreover, PS/rGO-PDA/Anti-CRP and PS/MNP-PDA/Anti-CRP immunosensors were also prepared and the effect of each component in the nanocomposite-based electrospun nanofiber (MNP, rGO) on the sensor response was investigated. The analytic performance of the developed PS/rGO-MNP-PDA/Anti-CRP, PS/rGO-PDA/Anti-CRP, and PS/MNP-PDA/Anti-CRP immunosensors were examined by performing electrochemical measurements in the presence of CRP. The linear detection range of PS/rGO-MNP-PDA/Anti-CRP immunosensor was found to be from 0.5 to 60 ng/mL and the limit of detection (LOD) was calculated as 0.33 ng/mL for CRP. The PS/rGO-MNP-PDA/Anti-CRP immunosensor also exhibited good repeatability with a low coefficient of variation. Full article
(This article belongs to the Special Issue New Opportunities for Emerging 2D Materials in Bioelectronics and Biosensors)
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<p>(<b>a</b>) FTIR spectrum of GO, rGO-MNP, rGO-MNP-PDA, (<b>b</b>) EDS spectrum of GO, (<b>c</b>) EDS spectrum of rGO-MNP, and (<b>d</b>) EDS spectrum of rGO-MNP-PDA.</p> Full article ">Figure 2
<p>SEM images (50.000× magnification) of (<b>a</b>) GO, (<b>b</b>) rGO-MNP, and (<b>c</b>) rGO-MNP-PDA.</p> Full article ">Figure 3
<p>SEM images (1000× magnification) of (<b>a</b>) PS (35%) (w/v), (<b>b</b>) PS/rGO-MNP-PDA 0.5% (w/v), (interior SEM images (25.000× magnifications); diameter distribution graphics of nanofibers (<b>c</b>) PS (35%) (w/v), (<b>d</b>) PS/rGO-MNP-PDA 0.5% (w/v) electrospun nanofibers, contact angle measurements of (<b>e</b>) PS (35%) (w/v), (<b>f</b>) PS/rGO-MNP-PDA 0.5% (w/v) electrospun nanofibers, and (<b>g</b>) EDS spectrum of PS/rGO-MNP-PDA 0.5% (w/v) electrospun nanofibers (electrospinning process conditions: 7.6 kV (applied voltage), 16 cm (TCD), and 1.2 mL/h (solution flow rate).</p> Full article ">Figure 4
<p>(<b>a</b>) CV profile, bare SPCE (1) PS/rGO-MNP-PDA (2) PS/rGO-MNP-PDA/Anti-CRP (3), and PS/rGO-MNP-PDA/Anti-CRP/CRP (4) (<b>b</b>) DPV profile, bare SPCE (1) PS/rGO-MNP-PDA (2) PS/rGO-MNP-PDA/Anti-CRP (3), and PS/rGO-MNP-PDA/Anti-CRP/CRP (4), (<b>c</b>) Nyquist profile, bare SPCE (1) PS/rGO-MNP-PDA (2) PS/rGO-MNP-PDA/Anti-CRP (3), and PS/rGO-MNP-PDA/Anti-CRP/CRP (4), (All measurements were carried out in 10 mL PBS (pH 7.4) including 0.1 M KCl and 5.0 mM HCF, [CRP] = 20 ng/mL).</p> Full article ">Figure 5
<p>(<b>a</b>) Impact of CRP concentration on the immunosensor current response, (<b>b</b>) Linear detection range of CRP, (<b>c</b>) CRP detection with PS/rGO-MNP-PDA/Anti-CRP, PS/rGO-PDA/Anti-CRP, and PS/MNP-PDA/Anti-CRP immunosensors. (All measurements, scan rate: 50 mVs<sup>−1</sup>, in the presence of 10 mL PBS (pH 7.4) solution including 5 mM HCF and 0.1 M KCl, [CRP] = 20 ng/mL. Error bars show the standard deviation of at least 3 replicate measurements).</p> Full article ">Figure 6
<p>Interference effect of BSA, urea, INS, and SAA on the detection of CRP using PS/rGO-MNP-PDA/Anti-CRP (in the presence of 10 mL PBS (pH 7.4) solution containing 5 mM HCF and 0.1 M KCl; [CRP]: 20 ng/mL, [BSA]; 1 mg/mL, [Urea]:0.5 mg/mL, [INS]: 1 µU/mL, [SAA]: 20 µg/mL; scan rate: 50 mVs<sup>−1</sup>.) Error bars show the standard deviation of at least 3 replicate measurements.</p> Full article ">Scheme 1
<p>Schematic representation of the preparation of PS/rGO-MNP-PDA/Anti-CRP modified SPCE and electrochemical detection of CRP.</p> Full article ">
15 pages, 4730 KiB  
Article
Comparison of Biosensing Methods Based on Different Isothermal Amplification Strategies: A Case Study with Erwinia amylovora
by Aleksandr V. Ivanov, Irina V. Safenkova, Natalia V. Drenova, Anatoly V. Zherdev and Boris B. Dzantiev
Biosensors 2022, 12(12), 1174; https://doi.org/10.3390/bios12121174 - 15 Dec 2022
Cited by 12 | Viewed by 2745
Abstract
Isothermal amplifications allow for the highly sensitive detection of nucleic acids, bypassing the use of instrumental thermal cycling. This work aimed to carry out an experimental comparison of the four most promising techniques: recombinase polymerase amplification (RPA) and loop-mediated isothermal amplification (LAMP) coupled [...] Read more.
Isothermal amplifications allow for the highly sensitive detection of nucleic acids, bypassing the use of instrumental thermal cycling. This work aimed to carry out an experimental comparison of the four most promising techniques: recombinase polymerase amplification (RPA) and loop-mediated isothermal amplification (LAMP) coupled with lateral flow test or coupled with additional amplification based on CRISPR/Cas12a resulting from the fluorescence of the Cas12a-cleaved probe. To compare the four amplification techniques, we chose the bacterial phytopathogen Erwinia amylovora (causative agent of fire blight), which has a quarantine significance in many countries and possesses a serious threat to agriculture. Three genes were chosen as the targets and primers were selected for each one (two for RPA and six for LAMP). They were functionalized by labels (biotin, fluorescein) at the 5′ ends for amplicons recognition by LFT. As a result, we developed LAMP-LFT, LAMP-CRISPR/Cas, RPA-LFT, and RPA-CRISPR/Cas for E. amylovora detection. The detection limit was 104 CFU/mL for LAMP-LFT, 103 CFU/mL for LAMP-CRISPR/Cas, and 102 CFU/mL for RPA-LFT and RPA-CRISPR/Cas. The results of four developed test systems were verified by qPCR on a panel of real samples. The developed assays based on RPA, LAMP, CRISPR/Cas12a, and LFT are rapid (30–55 min), user-friendly, and highly sensitive for E. amylovora detection. All proposed detection methods can be applied to fire blight diagnosis and effective management of this disease. Full article
(This article belongs to the Special Issue Microfluidic Biosensors for Point-of-Care Nucleic Acid Amplification Tests)
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Graphical abstract

Graphical abstract
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<p>Schemes of the biosensing techniques with isothermal amplifications compared in this study using the example of <span class="html-italic">E. amylovora</span> detection: (<b>A</b>) RPA-LFT; (<b>B</b>) RPA-CRISPR/Cas; (<b>C</b>) LAMP-LFT; and (<b>D</b>) LAMP-CRISPR/Cas.</p> Full article ">Figure 2
<p>Visualization of LAMP products after amplification of phosphoribosyl transferase gene (gene-1) (<b>A</b>) after electrophoresis in 1% agarose gel: 0, 10, 100, 1000, 10<sup>4</sup>, 10<sup>5</sup>, 10<sup>6</sup> copies per reaction, M1 means 100+ bp (100–1500 bp) DNA ladder, M2 means 1 kb (250–10,000 bp) DNA ladder; (<b>B</b>) using LFT recognized FAM/biotin labeled amplicons (BL-primer labeled with FAM, BIP primer labeled with biotin) “-” LAMP-LFT without the DNA template, “+” LAMP-LFT in the presence of 10<sup>4</sup> copies of the DNA template.</p> Full article ">Figure 3
<p>Results of amplification of the DNA fragments of gene-2 and gene-3. (<b>A</b>) RPA visualization of gene-2 amplicons on 2% agarose gel after electrophoresis; (<b>B</b>) RPA imaging of gene-3 amplicons on 2% agarose gel after electrophoresis; (<b>C</b>) test strips after RPA-LFT (the number of DNA copies in the reaction is indicated above the strips; 0 is the negative control, C means control zone, T means test zone) and the corresponding concentration dependences of the intensity of the test zones on a number of DNA copies; (<b>D</b>) Calibration plot of qPCR for DNA fragment of gene-3. Dash line indicates the limit cycle for reliable detection of gene-3.</p> Full article ">Figure 4
<p>Concentration dependences for <span class="html-italic">E. amylovora</span> cells used for detection by different amplification techniques: (<b>A</b>) LAMP-LFT; (<b>B</b>) LAMP-CRISPR/Cas; (<b>C</b>) RPA-LFT; and (<b>D</b>) RPA-CRISPR/Cas.</p> Full article ">Figure 4 Cont.
<p>Concentration dependences for <span class="html-italic">E. amylovora</span> cells used for detection by different amplification techniques: (<b>A</b>) LAMP-LFT; (<b>B</b>) LAMP-CRISPR/Cas; (<b>C</b>) RPA-LFT; and (<b>D</b>) RPA-CRISPR/Cas.</p> Full article ">Figure 5
<p>Detection of <span class="html-italic">E. amylovora</span> in plant leaves by qPCR, RPA-LFT, LAMP-LFT, RPA-CRISPR/Cas, and LAMP-CRISPR/Cas. The results for each sample are presented as distinct squares in the heat maps. (<b>A</b>) artificially contaminated extracts of plant samples and (<b>B</b>) real samples (dash lines in qPCR row mean no detectable signal). Positive samples are marked with “+”, negative samples are marked with “-” (cut-off for qPCR: 40; RPA-LFT and LAMP-LFT: 2.0; RPA-CRISPR/Cas: 3.18; LAMP-CRISPR/Cas: 0.34).</p> Full article ">
19 pages, 9767 KiB  
Review
Assessing the Food Quality Using Carbon Nanomaterial Based Electrodes by Voltammetric Techniques
by Shashanka Rajendrachari, Nagaraj Basavegowda, Vinayak M Adimule, Baris Avar, Prathap Somu, Saravana Kumar R. M. and Kwang-Hyun Baek
Biosensors 2022, 12(12), 1173; https://doi.org/10.3390/bios12121173 - 15 Dec 2022
Cited by 28 | Viewed by 4601
Abstract
The world is facing a global financial loss and health effects due to food quality adulteration and contamination, which are seriously affecting human health. Synthetic colors, flavors, and preservatives are added to make food more attractive to consumers. Therefore, food safety has become [...] Read more.
The world is facing a global financial loss and health effects due to food quality adulteration and contamination, which are seriously affecting human health. Synthetic colors, flavors, and preservatives are added to make food more attractive to consumers. Therefore, food safety has become one of the fundamental needs of mankind. Due to the importance of food safety, the world is in great need of developing desirable and accurate methods for determining the quality of food. In recent years, the electrochemical methods have become more popular, due to their simplicity, ease in handling, economics, and specificity in determining food safety. Common food contaminants, such as pesticides, additives, and animal drug residues, cause foods that are most vulnerable to contamination to undergo evaluation frequently. The present review article discusses the electrochemical detection of the above food contaminants using different carbon nanomaterials, such as carbon nanotubes (CNTs), graphene, ordered mesoporous carbon (OMC), carbon dots, boron doped diamond (BDD), and fullerenes. The voltammetric methods, such as cyclic voltammetry (CV) and differential pulse voltammetry (DPV), have been proven to be potential methods for determining food contaminants. The use of carbon-based electrodes has the added advantage of electrochemically sensing the food contaminants due to their excellent sensitivity, specificity, large surface area, high porosity, antifouling, and biocompatibility. Full article
(This article belongs to the Section Biosensor Materials)
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<p>Types of carbon materials and the overview of the article.</p> Full article ">Figure 2
<p>(<b>a</b>) Schematic representation of an multi walled carbon nanotube (MWCNT)modified graphite electrode for tyrosine detection, (<b>b</b>) Cyclic voltammograms collected at 0.48 mM tyramine solution at <b>a</b>: Tyrosinase/TiO<sub>2</sub>, <b>b</b>: Tyrosinase/TiO<sub>2</sub>/polycationic polymer/Nafion, <b>c</b>: Tyrosinase/TiO<sub>2</sub>/MWCNT/Nafion, and <b>d</b>: Tyrosinase/TiO<sub>2</sub>/MWCNT/polycationic polymer/Nafion biosensors. Reprinted (adapted)with permission from Ref. [<a href="#B36-biosensors-12-01173" class="html-bibr">36</a>]. Copyright 2016, Springer Nature.</p> Full article ">Figure 3
<p>Graphical representation of the whole experiment. Reprinted (adapted) with permission from Ref. [<a href="#B47-biosensors-12-01173" class="html-bibr">47</a>]. Copyright 2021, Springer Nature.</p> Full article ">Figure 4
<p>(<b>A</b>) The cyclic voltammograms of bare glassy carbon electrode (GCE), 5-amino-1,3,4-thiadiazole-2-thiol-Pt/glassy carbon electrode (ATDT-Pt/GCE), and electrochemically reduced graphene oxide-5-amino-1,3,4-thiadiazole-2-thiol-Pt/glassy carbon electrode (ERGO-ATDT-Pt/GCE) in 50 µM at a scan rate of 100 mV/s; (<b>B</b>) the plot of redox peak current vs. scan rate in presence at 30–300 mV/s; (<b>C</b>) CV at different concentrations of the orange II dye (0, 2, 5, 10, 20, 30, 50, and 70 µM) Reprinted (adapted) with permission from Ref. [<a href="#B61-biosensors-12-01173" class="html-bibr">61</a>]. Copyright 2015, Elsevier.</p> Full article ">Figure 5
<p>CV curve of catechol using a fabricated electrode at (<b>a</b>) 0.1 M PBS (pH 7) at the scan rate of 0.1 V/s. (<b>b</b>) Different scan rates from 0.1 to 0.3 V/s. (<b>c</b>) Different pH (6.5–8.0) with the sweep rate of 100 mV/s. Reprinted (adapted) with permission from Ref. [<a href="#B62-biosensors-12-01173" class="html-bibr">62</a>]. Copyright 2020, Bentham Science.</p> Full article ">Figure 6
<p>Fabrication process of the modified electrode. Reprinted (adapted) with permission from Ref. [<a href="#B65-biosensors-12-01173" class="html-bibr">65</a>]. Copyright 2017, Turkish Chemical Society.</p> Full article ">Figure 7
<p>Schematic representation of the silver nanoparticle/graphene nanoplatelets modified screen-printed carbon electrodes. Reprinted (adapted) with permission from Ref. [<a href="#B68-biosensors-12-01173" class="html-bibr">68</a>]. Copyright 2021, American Chemical Society.</p> Full article ">Figure 8
<p>Graphical representation of preparing fluorescent carbon dots on a modified electrode to determine methylmercury. Reprinted (adapted) with permission from [<a href="#B72-biosensors-12-01173" class="html-bibr">72</a>]. Copyright 2014, Elsevier.</p> Full article ">Figure 9
<p>CV curves of GCE (<b>a</b>,<b>b</b>), carbon dots/GCE (<b>c</b>,<b>d</b>) and carbob dots@Au/GCE (<b>e</b>,<b>f</b>) in the absence (<b>a</b>,<b>c</b>,<b>e</b>) and presence (<b>b</b>,<b>d</b>,<b>f</b>) of 0.2 mg/L ractopamine in a PBS (pH 7.0) solution. Reprinted (adapted) with permission from [<a href="#B76-biosensors-12-01173" class="html-bibr">76</a>]. Copyright 2020, ESG.</p> Full article ">
22 pages, 3754 KiB  
Review
State-of-the-Art Fluorescent Probes: Duplex-Specific Nuclease-Based Strategies for Early Disease Diagnostics
by Ghazala Ashraf, Zi-Tao Zhong, Muhammad Asif, Ayesha Aziz, Tayyaba Iftikhar, Wei Chen and Yuan-Di Zhao
Biosensors 2022, 12(12), 1172; https://doi.org/10.3390/bios12121172 - 15 Dec 2022
Cited by 7 | Viewed by 3092
Abstract
Precision healthcare aims to improve patient health by integrating prevention measures with early disease detection for prompt treatments. For the delivery of preventive healthcare, cutting-edge diagnostics that enable early disease detection must be clinically adopted. Duplex-specific nuclease (DSN) is a useful tool for [...] Read more.
Precision healthcare aims to improve patient health by integrating prevention measures with early disease detection for prompt treatments. For the delivery of preventive healthcare, cutting-edge diagnostics that enable early disease detection must be clinically adopted. Duplex-specific nuclease (DSN) is a useful tool for bioanalysis since it can precisely digest DNA contained in duplexes. DSN is commonly used in biomedical and life science applications, including the construction of cDNA libraries, detection of microRNA, and single-nucleotide polymorphism (SNP) recognition. Herein, following the comprehensive introduction to the field, we highlight the clinical applicability, multi-analyte miRNA, and SNP clinical assays for disease diagnosis through large-cohort studies using DSN-based fluorescent methods. In fluorescent platforms, the signal is produced based on the probe (dyes, TaqMan, or molecular beacon) properties in proportion to the target concentration. We outline the reported fluorescent biosensors for SNP detection in the next section. This review aims to capture current knowledge of the overlapping miRNAs and SNPs’ detection that have been widely associated with the pathophysiology of cancer, cardiovascular, neural, and viral diseases. We further highlight the proficiency of DSN-based approaches in complex biological matrices or those constructed on novel nano-architectures. The outlooks on the progress in this field are discussed. Full article
(This article belongs to the Special Issue Advances in Fluorescent Probe Biosensing)
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<p>(<b>A</b>) Schematic illustration of the target invasion-triggered signal amplification for miRNA identification. (<b>B</b>) The proposed miRNA detection mechanism. Functional separation of HMBs and tetrahedral nanoprobe assembly (<b>top panel</b>). The whole operational procedure (<b>bottom panel</b>). Reproduced with permission from Refs. [<a href="#B46-biosensors-12-01172" class="html-bibr">46</a>,<a href="#B47-biosensors-12-01172" class="html-bibr">47</a>], respectively. Copyright 2022, Elsevier.</p> Full article ">Figure 2
<p>(<b>a</b>) Schematic illustration of how miRNA (miR-21, let-7a) can be detected utilizing hydrogel microparticles that have been encoded with certain shapes using a smartphone detection platform. To learn more about the kind and concentration of the miRNA, the liquid system was moved to a solid substrate after the sandwich structure formation. (<b>b</b>) Images were then taken using a self-made POCT device and analyzed using Android software. Reprinted with permission from Ref. [<a href="#B59-biosensors-12-01172" class="html-bibr">59</a>]. Copyright 2019, American Chemical Society.</p> Full article ">Figure 3
<p>(<b>a</b>) Switch-conversional ratiometric fluorescence biosensor for miRNA let-7a detection, (<b>b</b>) illustration of exponential amplification reaction (EXPAR) and switch conversion. Reproduced with permission from Ref. [<a href="#B60-biosensors-12-01172" class="html-bibr">60</a>]. Copyright 2020, Elsevier.</p> Full article ">Figure 4
<p>Schematic representation of a fluorescence-quenching platform based on Mo<sub>2</sub>B nanosheets for imaging multiple miRNAs in living cells utilizing HCR amplification. Reprinted with permission from Ref. [<a href="#B72-biosensors-12-01172" class="html-bibr">72</a>]. Copyright 2022, Elsevier.</p> Full article ">Figure 5
<p>Schematic representation of the multiplexed sensing system using polychromatic tracks. Reprinted with permission from Ref. [<a href="#B73-biosensors-12-01172" class="html-bibr">73</a>]. Copyright 2021, Elsevier.</p> Full article ">Figure 6
<p>Scheme of the common DSNP assay for SNP detection. The green luminescent ball represents the first fluorescent donor, the red luminescent ball represents the second fluorescent donor, and the gray ball represents the fluorescent quencher. Reproduced with permission from Ref. [<a href="#B100-biosensors-12-01172" class="html-bibr">100</a>].</p> Full article ">Figure 7
<p>Investigation of <span class="html-italic">p53 C309T</span>, prothrombin <span class="html-italic">20210 G-to-A</span>, and <span class="html-italic">MTHFR C677T</span> polymorphous sites in homozygous and heterozygous DNA via the DSNP assay. (<b>A</b>) Images obtained with the fluorescent stereomicroscope equipped with green (G) and red (R) filters. GR: computer superposition of images obtained with green and red filters; n/n: homozygous DNA samples comprising wild-type sequence variant; n/m: heterozygous DNA samples; m/m: homozygous DNA samples comprising mutant sequence variant. (<b>B</b>) Normalized emission spectra of these samples obtained by the spectrofluorometer, with excitation wavelengths at 480/550 nm for green/red fluorescence, respectively. Green line: homozygous DNA samples comprising the wild-type sequence variant; red line: homozygous DNA samples comprising the mutant sequence; blue line: heterozygous DNA samples. Reprinted with permission from Ref. [<a href="#B100-biosensors-12-01172" class="html-bibr">100</a>].</p> Full article ">Figure 8
<p>Schemes for detecting single-base mismatches without using optical labels. (<b>A</b>) The scheme was illustrated in the case of the S1 nuclease, and various DNA structures were used. S1 nuclease hydrolyzes mismatched DNA duplexes to 5′-phosphoryl-terminated products, but not perfectly matched duplexes. (<b>B</b>) Diagram of the DSN-based, label-free optical detection of SNPs. Lower left: images of the AuNP solution containing the probe, S1 nuclease, and various targets (PM or 1MM). Images of AuNP solution containing a probe, various targets, and duplex-specific nuclease are shown on the right bottom (DSN). The assays were incubated at 65 °C for 7.5 min before adding AuNP and the salt solution. Photographs were taken 2 min after a 40 µL salt solution was added. Reproduced with permission from Ref. [<a href="#B112-biosensors-12-01172" class="html-bibr">112</a>]. Copyright 2011, Elsevier.</p> Full article ">
20 pages, 3076 KiB  
Review
Optical Methods for Label-Free Detection of Bacteria
by Pengcheng Wang, Hao Sun, Wei Yang and Yimin Fang
Biosensors 2022, 12(12), 1171; https://doi.org/10.3390/bios12121171 - 15 Dec 2022
Cited by 18 | Viewed by 4626
Abstract
Pathogenic bacteria are the leading causes of food-borne and water-borne infections, and one of the most serious public threats. Traditional bacterial detection techniques, including plate culture, polymerase chain reaction, and enzyme-linked immunosorbent assay are time-consuming, while hindering precise therapy initiation. Thus, rapid detection [...] Read more.
Pathogenic bacteria are the leading causes of food-borne and water-borne infections, and one of the most serious public threats. Traditional bacterial detection techniques, including plate culture, polymerase chain reaction, and enzyme-linked immunosorbent assay are time-consuming, while hindering precise therapy initiation. Thus, rapid detection of bacteria is of vital clinical importance in reducing the misuse of antibiotics. Among the most recently developed methods, the label-free optical approach is one of the most promising methods that is able to address this challenge due to its rapidity, simplicity, and relatively low-cost. This paper reviews optical methods such as surface-enhanced Raman scattering spectroscopy, surface plasmon resonance, and dark-field microscopic imaging techniques for the rapid detection of pathogenic bacteria in a label-free manner. The advantages and disadvantages of these label-free technologies for bacterial detection are summarized in order to promote their application for rapid bacterial detection in source-limited environments and for drug resistance assessments. Full article
(This article belongs to the Special Issue Label-Free Biosensor)
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<p>Schematic diagrams of (<b>a</b>) SPR optical system and (<b>b</b>) SPR microscopy.</p> Full article ">Figure 2
<p>Schematic diagram of the rapid antimicrobial susceptibility test at single bacteria level using SPR microscopy [<a href="#B51-biosensors-12-01171" class="html-bibr">51</a>]. Adapted with permission from Ref. [<a href="#B51-biosensors-12-01171" class="html-bibr">51</a>]. Copyright © 2015 American Chemical Society.</p> Full article ">Figure 3
<p>Schematic detection principle of <span class="html-italic">E. coli</span> hydrogenated amorphous silicon a-Si:H surface modified with anti-fimbrial antibodies against the major pilin protein fimA. (<b>a</b>) Surface structures of <span class="html-italic">E. coli</span> expressing fimA selectively captured and positively charged Au-NRs incubated with <span class="html-italic">E. coli</span> for SERS sensing. (<b>b</b>) Anti-fimbriae modified array, optical imaging of spots after interaction with <span class="html-italic">E. coli</span> and SERS spectra after capturing bacteria [<a href="#B97-biosensors-12-01171" class="html-bibr">97</a>]. Adapted with permission from Ref. [<a href="#B97-biosensors-12-01171" class="html-bibr">97</a>]. Copyright © 2020 Elsevier B.V.</p> Full article ">Figure 4
<p>Schematic and detection principle of GNP/monolith modified substrate for the capture of <span class="html-italic">E. coli</span>. (<b>a</b>) Cross-sectional view of <span class="html-italic">E. coli</span> captured on gold nanoparticles modified substrates. (<b>b</b>) SERS enhancement factor of porous substrate functionalized with 40 nm gold nanoparticles simulated by FDTD. (<b>c</b>) In the simulation, the geometry of the model is reduced to two hemispheres coated with 40 nm spherical gold nanoparticles, separated by 10 nm; the electric field intensity distributions in x-y plane and y-z plane of gold on porous monolithic substrate excited by 633 nm laser are calculated. (<b>d</b>) SERS spectra of 40 nm gold nanoparticles/substrate functionalized with cysteamine [<a href="#B98-biosensors-12-01171" class="html-bibr">98</a>]. Adapted with permission from Ref. [<a href="#B98-biosensors-12-01171" class="html-bibr">98</a>]. Copyright © 2015 Elsevier B.V.</p> Full article ">Figure 5
<p>Schematic diagram of counting <span class="html-italic">E. coli</span> under dark-field, using antibody functionalization of MNP to form a gold ring structure around <span class="html-italic">E. coli.</span> (<b>a</b>) MNP probe was obtained by culture of <span class="html-italic">E. coli</span> antibody onto MNP. <span class="html-italic">E. coli</span> samples are first mixed with MNP probes to form probe-<span class="html-italic">E. coli</span> complexes. (<b>b</b>)The complex of <span class="html-italic">E. coli</span> and MNP probes was separated by a magnet and then counted under a dark-field microscope. [<a href="#B119-biosensors-12-01171" class="html-bibr">119</a>]. Adapted with permission from Ref. [<a href="#B119-biosensors-12-01171" class="html-bibr">119</a>]. Copyright © 2018 The Author(s).</p> Full article ">Figure 6
<p>Schematic of detection of <span class="html-italic">E. coli</span> with dark-field microscopy. (<b>a</b>) Samples containing <span class="html-italic">E. coli.</span> (b) an anti-<span class="html-italic">E. coli</span> antibody functionalized gold surface. (<b>c</b>) Dark-field microscopy is used to inspect the surface of the gold sheet after 75 min incubation with the field sample and rinse with phosphate buffer solution, enlarging the image. (<b>d</b>) Statistical image analysis was used to count the bacteria captured by the antibodies [<a href="#B40-biosensors-12-01171" class="html-bibr">40</a>]. Adapted with permission from Ref. [<a href="#B40-biosensors-12-01171" class="html-bibr">40</a>]. Copyright © 2019 MDPI.</p> Full article ">Figure 7
<p>Bacteria detection principle by a single-particle imaging approach. (<b>a</b>) Schematic diagram of bacteria detection by single-particle imaging. (<b>b</b>) The inhomogeneity of particle morphology is identified by tracking the fluctuations of scattering intensity in free solution. (<b>c</b>) Convection induced by an electric heater was used to screen individual bacteria in a small field of view [<a href="#B121-biosensors-12-01171" class="html-bibr">121</a>]. Adapted with permission from Ref. [<a href="#B121-biosensors-12-01171" class="html-bibr">121</a>]. Copyright © 2022 The Author(s).</p> Full article ">Figure 8
<p>The principle of tracking the rapid identification of 1 um polystyrene spheres and single cell phenotypic characteristics of <span class="html-italic">E. coli</span>. (<b>a</b>) <span class="html-italic">E. coli</span> rotation-induced scattering intensity fluctuation tracking compared with 1 µm microbeads. (<b>b</b>) SVM classification result of one representative infection negative sample. (<b>c</b>) SVM classification result of one representative infection positive sample. [<a href="#B122-biosensors-12-01171" class="html-bibr">122</a>]. Adapted with permission from Ref. [<a href="#B122-biosensors-12-01171" class="html-bibr">122</a>]. Copyright © 2022 American Chemical Society.</p> Full article ">Figure 9
<p>Schematic illustration of the experimental arrangement. (<b>a</b>) Covalent binding of phage to SiO<sub>2</sub> on fiber surface. (<b>b</b>) Resonance wavelength change with analyte refractive index transmission spectrum [<a href="#B124-biosensors-12-01171" class="html-bibr">124</a>]. Adapted with permission from Ref. [<a href="#B124-biosensors-12-01171" class="html-bibr">124</a>]. Copyright © 2012 Elsevier B.V.</p> Full article ">
13 pages, 1735 KiB  
Article
Estimation of Respiratory Rate from Functional Near-Infrared Spectroscopy (fNIRS): A New Perspective on Respiratory Interference
by Naser Hakimi, Mohammad Shahbakhti, Sofia Sappia, Jörn M. Horschig, Mathijs Bronkhorst, Marianne Floor-Westerdijk, Gaetano Valenza, Jeroen Dudink and Willy N. J. M. Colier
Biosensors 2022, 12(12), 1170; https://doi.org/10.3390/bios12121170 - 14 Dec 2022
Cited by 6 | Viewed by 2953
Abstract
Objective: Respiration is recognized as a systematic physiological interference in functional near-infrared spectroscopy (fNIRS). However, it remains unanswered as to whether it is possible to estimate the respiratory rate (RR) from such interference. Undoubtedly, RR estimation from fNIRS can provide complementary information that [...] Read more.
Objective: Respiration is recognized as a systematic physiological interference in functional near-infrared spectroscopy (fNIRS). However, it remains unanswered as to whether it is possible to estimate the respiratory rate (RR) from such interference. Undoubtedly, RR estimation from fNIRS can provide complementary information that can be used alongside the cerebral activity analysis, e.g., sport studies. Thus, the objective of this paper is to propose a method for RR estimation from fNIRS. Our primary presumption is that changes in the baseline wander of oxygenated hemoglobin concentration (O2Hb) signal are related to RR. Methods: fNIRS and respiratory signals were concurrently collected from subjects during controlled breathing tasks at a constant rate from 0.1 Hz to 0.4 Hz. Firstly, the signal quality index algorithm is employed to select the best O2Hb signal, and then a band-pass filter with cut-off frequencies from 0.05 to 2 Hz is used to remove very low- and high-frequency artifacts. Secondly, troughs of the filtered O2Hb signal are localized for synthesizing the baseline wander (S1) using cubic spline interpolation. Finally, the fast Fourier transform of the S1 signal is computed, and its dominant frequency is considered as RR. In this paper, two different datasets were employed, where the first one was used for the parameter adjustment of the proposed method, and the second one was solely used for testing. Results: The low mean absolute error between the reference and estimated RRs for the first and second datasets (2.6 and 1.3 breaths per minute, respectively) indicates the feasibility of the proposed method for RR estimation from fNIRS. Significance: This paper provides a novel view on the respiration interference as a source of complementary information in fNIRS. Full article
(This article belongs to the Special Issue Optical Biosensing and Bioimaging)
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<p>The block diagram of the proposed method. It should be noted that for the sake of clarity, fNIRS signals are shown only for 10 s.</p> Full article ">Figure 2
<p>An example of <math display="inline"><semantics> <mrow> <msub> <mi>O</mi> <mn>2</mn> </msub> <mi>H</mi> <mi>b</mi> </mrow> </semantics></math> signal with its corresponding peaks (blue) and troughs (black).</p> Full article ">Figure 3
<p>An example of the trough detection. The filtered <math display="inline"><semantics> <mrow> <msub> <mi>O</mi> <mn>2</mn> </msub> <mi>H</mi> <mi>b</mi> </mrow> </semantics></math> signal (<b>a</b>), the selected troughs after employing <math display="inline"><semantics> <mrow> <mi>T</mi> <msub> <mi>h</mi> <mn>1</mn> </msub> </mrow> </semantics></math> (<b>b</b>), and <math display="inline"><semantics> <mrow> <mi>T</mi> <msub> <mi>h</mi> <mn>2</mn> </msub> </mrow> </semantics></math> (<b>c</b>).</p> Full article ">Figure 4
<p>The FFT of the baseline wander before (<b>a</b>) and after (<b>b</b>) employing the MA filtering. The red dot stands for dominant frequency in the FFT domain. Note that the reference RR is 0.4 Hz in this example.</p> Full article ">Figure 5
<p>Data recording protocol. It consists of a resting period for 60 s (<b>A</b>), and two breathing control tasks lasting for 250 s (<b>B</b>,<b>D</b>), which are separated by a 30 s resting period (<b>C</b>). Subsequently, the same blocks were repeated (<b>E</b>–<b>H</b>).</p> Full article ">Figure 6
<p>fNIRS optode placement for dataset I (<b>a</b>) and dataset II (<b>b</b>).</p> Full article ">Figure 7
<p>Regulation of constants for trough detection in terms of mean±std of the CSI. <math display="inline"><semantics> <mrow> <mi>T</mi> <msub> <mi>h</mi> <mn>1</mn> </msub> </mrow> </semantics></math> (<b>a</b>) and <math display="inline"><semantics> <mrow> <mi>T</mi> <msub> <mi>h</mi> <mn>2</mn> </msub> </mrow> </semantics></math> (<b>b</b>).</p> Full article ">Figure 8
<p>An example of the filtered <math display="inline"><semantics> <mrow> <msub> <mi>O</mi> <mn>2</mn> </msub> <mi>H</mi> <mi>b</mi> </mrow> </semantics></math> signal, the corresponding baseline wanders, and the reference respiratory signal.</p> Full article ">Figure 9
<p>The Bland–Altman plot of the estimated RRs on dataset II.</p> Full article ">
12 pages, 2652 KiB  
Article
Gold Leaf-Based Microfluidic Platform for Detection of Essential Oils Using Impedance Spectroscopy
by Ankita Sinha, Adrian K. Stavrakis, Mitar Simić, Sanja Kojić and Goran M. Stojanović
Biosensors 2022, 12(12), 1169; https://doi.org/10.3390/bios12121169 - 14 Dec 2022
Cited by 2 | Viewed by 2193
Abstract
Drug delivery systems are engineered platforms for the controlled release of various therapeutic agents. This paper presents a conductive gold leaf-based microfluidic platform fabricated using xurography technique for its potential implication in controlled drug delivery operations. To demonstrate this, peppermint and eucalyptus essential [...] Read more.
Drug delivery systems are engineered platforms for the controlled release of various therapeutic agents. This paper presents a conductive gold leaf-based microfluidic platform fabricated using xurography technique for its potential implication in controlled drug delivery operations. To demonstrate this, peppermint and eucalyptus essential oils (EOs) were selected as target fluids, which are best known for their medicinal properties in the field of dentistry. The work takes advantage of the high conductivity of the gold leaf, and thus, the response characteristics of the microfluidic chip are studied using electrochemical impedance spectroscopy (EIS) upon injecting EOs into its micro-channels. The effect of the exposure time of the chip to different concentrations (1% and 5%) of EOs was analyzed, and change in electrical resistance was measured at different time intervals of 0 h (the time of injection), 22 h, and 46 h. It was observed that our fabricated device demonstrated higher values of electrical resistance when exposed to EOs for longer times. Moreover, eucalyptus oil had stronger degradable effects on the chip, which resulted in higher electrical resistance than that of peppermint. 1% and 5% of Eucalyptus oil showed an electrical resistance of 1.79 kΩ and 1.45 kΩ at 10 kHz, while 1% and 5% of peppermint oil showed 1.26 kΩ and 1.07 kΩ of electrical resistance at 10 kHz respectively. The findings obtained in this paper are beneficial for designing suitable microfluidic devices to expand their applications for various biomedical purposes. Full article
(This article belongs to the Special Issue Biosensor Nanoengineering: Design, Operation and Implementation)
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<p>Exploded view of multi-layered Au leaf-based microfluidic chip design and layout of the study performed to analyze the electrical performance of the fabricated device under the influence of essential oil using impedance measurements.</p> Full article ">Figure 2
<p>(<b>a</b>) FTIR spectrum and (<b>b</b>) SEM micrograph of the cross-section of Au leaf used for fabrication of microfluidic chip.</p> Full article ">Figure 3
<p>Nanoindentation results of microfluidic chip with (<b>a</b>–<b>d</b>) 1% and 5% of peppermint and eucalyptus essentials oils, (<b>e</b>) nanoindentation of dry chip before using any liquid sample, (<b>f</b>) average values of all samples.</p> Full article ">Figure 4
<p>Electrical resistance of Au-based microfluidic chip as a function of frequency at different time intervals under the influence of (<b>a</b>) 1% peppermint, (<b>b</b>) 5% peppermint, (<b>c</b>) 1% eucalyptus, (<b>d</b>) 5% eucalyptus essential oils.</p> Full article ">Figure 5
<p>Electrical resistance of fabricated microfluidic chip for 1% and 5% concentrations of (<b>a</b>) peppermint and (<b>b</b>) eucalyptus EOs, after 46 h of exposure.</p> Full article ">Figure 6
<p>Change in resistance of fabricated microfluidic chip under the influence of (<b>a</b>) 1% and (<b>b</b>) 5% EOs solutions after 46 h of exposure.</p> Full article ">
15 pages, 1550 KiB  
Article
Noninvasive Monitoring of Simulated Hemorrhage and Whole Blood Resuscitation
by Jay F. Gupta, Saaid H. Arshad, Brian A. Telfer, Eric J. Snider and Victor A. Convertino
Biosensors 2022, 12(12), 1168; https://doi.org/10.3390/bios12121168 - 14 Dec 2022
Cited by 6 | Viewed by 2461
Abstract
Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. [...] Read more.
Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators because of physiological mechanisms that compensate for blood loss and thus do not provide an accurate assessment of volume status. As an alternative, machine learning (ML) algorithms that operate on an arterial blood pressure (ABP) waveform have been shown to provide an effective early indicator. However, these ML approaches lack physiological interpretability. In this paper, we evaluate and compare the performance of ML models trained on nine ABP-derived features that provide physiological insight, using a database of 13 human subjects from a lower-body negative pressure (LBNP) model of progressive central hypovolemia and subsequent progressive restoration to normovolemia (i.e., simulated hemorrhage and whole blood resuscitation). Data were acquired at multiple repressurization rates for each subject to simulate varying resuscitation rates, resulting in 52 total LBNP collections. This work is the first to use a single ABP-based algorithm to monitor both simulated hemorrhage and resuscitation. A gradient-boosted regression tree model trained on only the half-rise to dicrotic notch (HRDN) feature achieved a root-mean-square error (RMSE) of 13%, an R2 of 0.82, and area under the receiver operating characteristic curve of 0.97 for detecting decompensation. This single-feature model’s performance compares favorably to previously reported results from more-complex black box machine learning models. This model further provides physiological insight because HRDN represents an approximate measure of the delay between the ABP ejected and reflected wave and therefore is an indication of cardiac and peripheral vascular mechanisms that contribute to the compensatory response to blood loss and replacement. Full article
(This article belongs to the Topic AI-enabled Sensor-related Technologies for Physiological Monitoring Designed to Advance Emergency Medical Care)
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<p>Representative negative pressure application protocol and reference CRM (RCRM) values during the baseline, depressurization, repressurization, and recovery phases of the LBNP studies. The duration of each phase varied based on the ramp speed selected for that trial.</p> Full article ">Figure 2
<p>Landmark points on Finapres ABP pulse corresponding to (A) start of pulse, (B) systolic half-rise, (C) systolic peak, (D) dicrotic notch, (E) end of pulse, and (F) systolic peak of successive pulse.</p> Full article ">Figure 3
<p>Box plots showing statistical characteristics of the RMSEs binned per ramp speed. The median (red line), 25th and 75th percentiles (top and bottom edges of the blue box), outliers (red crosses), and valid maximum and minimum values that were not classified as outliers (whiskers). The top plot shows the RMSEs for the all-features GB tree model binned without baseline normalization per ramp speed, while the bottom plot shows the same data for the model that only uses HRDN.</p> Full article ">Figure 4
<p>Gini importance plots for full procedure model, simulated hemorrhage model, simulated resuscitation model, and step hemorrhage LBNP protocol model [<a href="#B26-biosensors-12-01168" class="html-bibr">26</a>]. These plots are for the GB tree model trained using all features without any baseline normalization.</p> Full article ">Figure 5
<p>(<b>a</b>) A BA plot is shown for the GB tree model trained using all features without baseline normalization for all subjects, and (<b>b</b>) Sample reference CRM and estimated CRM using a GB tree model trained with only the HRDN feature from subject 4 without baseline normalization. This example has a lower than average RMSE of 9.9%.</p> Full article ">
21 pages, 2678 KiB  
Review
A Review: Research Progress of Neural Probes for Brain Research and Brain–Computer Interface
by Jiahui Luo, Ning Xue and Jiamin Chen
Biosensors 2022, 12(12), 1167; https://doi.org/10.3390/bios12121167 - 14 Dec 2022
Cited by 14 | Viewed by 4034
Abstract
Neural probes, as an invasive physiological tool at the mesoscopic scale, can decipher the code of brain connections and communications from the cellular or even molecular level, and realize information fusion between the human body and external machines. In addition to traditional electrodes, [...] Read more.
Neural probes, as an invasive physiological tool at the mesoscopic scale, can decipher the code of brain connections and communications from the cellular or even molecular level, and realize information fusion between the human body and external machines. In addition to traditional electrodes, two new types of neural probes have been developed in recent years: optoprobes based on optogenetics and magnetrodes that record neural magnetic signals. In this review, we give a comprehensive overview of these three kinds of neural probes. We firstly discuss the development of microelectrodes and strategies for their flexibility, which is mainly represented by the selection of flexible substrates and new electrode materials. Subsequently, the concept of optogenetics is introduced, followed by the review of several novel structures of optoprobes, which are divided into multifunctional optoprobes integrated with microfluidic channels, artifact-free optoprobes, three-dimensional drivable optoprobes, and flexible optoprobes. At last, we introduce the fundamental perspectives of magnetoresistive (MR) sensors and then review the research progress of magnetrodes based on it. Full article
(This article belongs to the Section Biosensor and Bioelectronic Devices)
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<p>Different strategies for electrode flexibility. (<b>a</b>) Schematic of the electrode with flexible Parylene probes [<a href="#B52-biosensors-12-01167" class="html-bibr">52</a>]. Reprinted under a Creative Commons Attribution (CC BY) license. (<b>b</b>) Optical micrographs of the flexible SU-8 probe with 90° bending and penetrate an agar gel. Reprinted from [<a href="#B54-biosensors-12-01167" class="html-bibr">54</a>], Copyright (2013), with permission from Elsevier. (<b>c</b>) Microscope image of the fishbone-shaped polyimide neural probe [<a href="#B55-biosensors-12-01167" class="html-bibr">55</a>]. (<b>d</b>) Nanomaterials PEDOT-CNF to improve electrode performance: A—Optical micrograph of the neural probe; B—Schematic diagram of the PEDOT-CNF composite deposition [<a href="#B66-biosensors-12-01167" class="html-bibr">66</a>]. Reprinted under a Creative Commons Attribution (CC BY) license.</p> Full article ">Figure 2
<p>Different kinds of multifunctional optoprobes integrating optical stimulation, electrical recording, and microfluidic channels. (<b>a</b>) Multifunctional coaxial polymer fiber-based optoprobe achieved by thermal drawing process. Refractive index difference between medical-grade materials- polycarbonate (PC) and cyclic olefin copolymer (COC) allow light to be confined within PC, while the polymer composite-conductive polyethylene (CPE) is used as recording electrode [<a href="#B94-biosensors-12-01167" class="html-bibr">94</a>]. (<b>b</b>) Micrographs of two multifunctional optoprobes with different waveguide output surfaces (flat, concave) which might influence the light propagation in tissue [<a href="#B95-biosensors-12-01167" class="html-bibr">95</a>]. (<b>c</b>) View of the multifunctional MEMS two-dimensional multi-handle waveguide-based optoprobes. Reprinted under a Creative Commons Attribution (CC BY) license [<a href="#B96-biosensors-12-01167" class="html-bibr">96</a>]. (<b>d</b>) Schematic illustrations showing three 2D multifunctional optoprobes before stacking and bonding (left), assembled 3D high-density multifunctional array (middle) [<a href="#B97-biosensors-12-01167" class="html-bibr">97</a>]. Reprinted under a Creative Commons Attribution (CC BY) license.</p> Full article ">Figure 3
<p>Several types of three-dimensional drivable optoprobes. (<b>a</b>) Photograph of the 3D optoprobe with a custom microdrive [<a href="#B97-biosensors-12-01167" class="html-bibr">97</a>]. Reprinted under a Creative Commons Attribution (CC BY) license. (<b>b</b>) Design of multisite drivable fiber-based optrode arrays. (<b>A</b>): Schematic diagram of a 32-channel drivable optrode array; (<b>B</b>): Detailed schematic diagram and top view (<b>C</b>) of the optrode tip; (<b>D</b>): Photos of a 64-channel multisite drivable optrode array and the optrode tip (<b>E</b>); (<b>F</b>): Optogenetic stimulation and electrophysiological recording using a multisite drivable optrode array implanted in a freely moving mouse [<a href="#B116-biosensors-12-01167" class="html-bibr">116</a>]. Reprinted under a Creative Commons Attribution (CC BY) license. (<b>c</b>) The fully prepared drivable optrode with the 3D-printed acrylic microdrive and magnified view of the optrode tip. (<b>A</b>): Front view of the drivable optrode; (<b>B</b>): Magnified image of the optrode tip [<a href="#B122-biosensors-12-01167" class="html-bibr">122</a>]. Reprinted from, copyright (2021), with permission from Elsevier. (<b>d</b>) Schematic diagram of the completely constructed 3D high-density drivable optrode array. (<b>A</b>): Explosive view of four 2D high-density probes; (<b>B</b>): Schematic diagram of the completely constructed 3D high-density drivable optrode array. Reprinted with permission from [<a href="#B121-biosensors-12-01167" class="html-bibr">121</a>]. Copyright 2021 American Chemical Society.</p> Full article ">Figure 4
<p>Several ways to implement flexible optoprobes: (<b>a</b>) Schematics of the cylindrical flexible optoprobe, in which a PI-based ribbon cable interconnects a bare LED chip by flip-chip bonding to respective connector pads. Close-up view showing the placement of the LED chip in a cylindrical transparent optoprobe between two macroelectrodes [<a href="#B132-biosensors-12-01167" class="html-bibr">132</a>]. (<b>b</b>) Optoprobes design for transfer of printed μ-LED from sapphire wafer to a flexible polyester substrate: (<b>A</b>): Schematic diagram of its multi-layer structure; (<b>B</b>): Integrated system wirelessly powered with RF scavenging [<a href="#B134-biosensors-12-01167" class="html-bibr">134</a>]. (<b>c</b>) Schematic illustration of the probe architecture with integrated GaN µ-LEDs and recording electrodes on a flexible substrate using a standard microfabrication process. (<b>A</b>): Schematic illustration of the probe architecture with a flexible Parylene C cable; (<b>B</b>): Schematic cross-section of active region [<a href="#B127-biosensors-12-01167" class="html-bibr">127</a>]. Reprinted under a Creative Commons Attribution (CC BY) license.</p> Full article ">Figure 5
<p>(<b>a</b>) Schematic representation of the GMR element, where current is flowing parallel to the film. (<b>b</b>) Schematic illustration of the two current models of GMR effect, explaining spin-dependent scattering of parallel magnetization and anti-parallel magnetization at the interface between FM and NM metal layers. Black arrows: the magnetization state of the FM layer. Blue arrows: trajectories of spin-up conduction electron. Red arrow: trajectories of spin-down conduction electron.</p> Full article ">Figure 6
<p>(<b>a</b>) Schematic representation of the TMR element, where current is flowing perpendicular to the film. (<b>b</b>) Spin subbands of parallel and anti-parallel magnetizations of FM materials. Black arrows: the magnetization state of the FM layer. Blue arrows: trajectories of spin-up conduction electron. Red arrow: trajectories of spin-down conduction electron. Thick (thin) arrows indicate high (low) spin currents.</p> Full article ">Figure 7
<p>(<b>a</b>) Hippocampus slice with the relative position of the sensor array with respect to the hippocampus structure. Red arrows represent the hippocampal network forming a well-characterized closed loop due to synaptic and action potential sources [<a href="#B155-biosensors-12-01167" class="html-bibr">155</a>]. Reprinted with permission from AIP Publishing 2011. (<b>b</b>) SEM image of the needles array and the tip of the needle with a well-defined SV sensor [<a href="#B157-biosensors-12-01167" class="html-bibr">157</a>]. (<b>c</b>) Schematic representation of the in vivo experimental set-up to record neural responses from rat cerebral cortex [<a href="#B22-biosensors-12-01167" class="html-bibr">22</a>]. Reprinted with permission from Elsevier 2017. (<b>d</b>) Schematic view of the sharp magnetrodes with orthogonal and parallel configurations and the position of SV1, SV2, and gold electrode along the probe. The arrows indicate the sensing direction [<a href="#B159-biosensors-12-01167" class="html-bibr">159</a>]. (<b>e</b>) Photograph of the GMR magnetrode with orthogonally sensitive directions. Reprinted with permission from [<a href="#B161-biosensors-12-01167" class="html-bibr">161</a>]. Copyright 2020 American Chemical Society.</p> Full article ">
14 pages, 4714 KiB  
Article
Triple Enhancement for Sensitive Immunochromatographic Assay: A Case Study for Human Fatty Acid-Binding Protein Detection
by Nadezhda A. Taranova, Alisa A. Bulanaya, Anatoly V. Zherdev and Boris B. Dzantiev
Biosensors 2022, 12(12), 1166; https://doi.org/10.3390/bios12121166 - 14 Dec 2022
Viewed by 1842
Abstract
The work considers a combination of three enhancing approaches for immunochromatographic assay (ICA) and the integration of their impacts into changes of the limit of detection (LOD). Human fatty acid binding protein (FABP), an early biomarker of acute myocardial infarction, was the target [...] Read more.
The work considers a combination of three enhancing approaches for immunochromatographic assay (ICA) and the integration of their impacts into changes of the limit of detection (LOD). Human fatty acid binding protein (FABP), an early biomarker of acute myocardial infarction, was the target analyte. Starting from the common ICA protocol with an LOD equal to 11.2 ng/mL, three approaches were realized: (1) replacement of spherical gold nanoparticles with gold nanoflowers having a branched surface (20-fold lowering the LOD); (2) enhanced labeling of immune complexes via nanoparticle aggregates (15-fold lowering); (3) in-situ growth of bound nanoparticles by reduction of gold salts (3-fold lowering). Single and combined implementations of these approaches have been studied. It has been shown that the LOD decrease for combined approaches is close to the multiplied contribution of each of them. The final LOD for FABP was 0.05 ng/mL, which is 220 times lower than the LOD for the common ICA protocol. The efficiency of the enhanced ICA with three combined approaches was confirmed by testing human serum samples for FABP presence and content. The development presents a new efficient technique for rapid sensitive detection of FABP for medical diagnostics. Moreover, the demonstrated multiple enhancements could be applied for various demanded analytes. Full article
(This article belongs to the Special Issue Nanobiosensors and Immunoassay)
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<p>Scheme of test strip for ICA with triple enhancement: 1—plastic support, 2—sample pad, 3—pad with mixture of GNFs conjugates with biotinylated proteins, 4—pad with sGNP-streptavidin conjugate, 5—working membrane, 6—analytical zone, 7—control zone, 8—absorbent pad.</p> Full article ">Figure 2
<p>sGNPs: TEM image (<b>A</b>) and distribution of diameters (<b>B</b>).</p> Full article ">Figure 3
<p>Dependence of the intensity of coloration of the analytical zone of the test strip on the nature of detergents (used at 1% concentration) (<b>A</b>) and the detergent concentration (for the case of Tween 20 use) (<b>B</b>) (<span class="html-italic">n</span> = 5).</p> Full article ">Figure 4
<p>Common ICA protocol: Appearance of test strips (<b>A</b>), SEM image of the sGNPs bound in the analytical zone (<b>B</b>) and calibration curve for FABP detection in serum (<b>C</b>) (<span class="html-italic">n</span> = 5).</p> Full article ">Figure 5
<p>GNFs preparation: TEM images of the nuclei (<b>A</b>) and the final GNFs (<b>B</b>).</p> Full article ">Figure 6
<p>Choice of ICA conditions for test systems with GNPs and GNFs. Dependence of cut off values on the concentration of F5/FABP applied in the analytical zone (<b>A</b>) and on the optical density of the used antibody-nanoparticles conjugates solutions (<b>B</b>) (<span class="html-italic">n</span> = 5).</p> Full article ">Figure 7
<p>ICA with the use of GNFs: Appearance of test strips (<b>A</b>), SEM image of the GNFs bound in the analytical zone (<b>B</b>) and calibration curve for FABP detection in serum (<b>C</b>) (<span class="html-italic">n</span> = 5).</p> Full article ">Figure 8
<p>ICA with the use of GNPs aggregation: Appearance of test strips (<b>A</b>), SEM image of the sGNPs aggregates bound in the analytical zone (<b>B</b>) and calibration curve for FABP detection in serum (<b>C</b>) (<span class="html-italic">n</span> = 5).</p> Full article ">Figure 9
<p>ICA with the use of GNFs aggregation: Appearance of test strips (<b>A</b>), SEM image of the sGNFs aggregates bound in the analytical zone (<b>B</b>) and calibration curve for FABP detection in serum (<b>C</b>) (<span class="html-italic">n</span> = 5).</p> Full article ">Figure 10
<p>ICA with triple enhancing approaches: appearance of test strips (<b>A</b>), SEM image of the sGNFs aggregates bound in the analytical zone (<b>B</b>) and calibration curve for FABP detection in serum (<b>C</b>) (<span class="html-italic">n</span> = 5).</p> Full article ">
13 pages, 2248 KiB  
Article
Ultrasensitive Determination of Glial-Fibrillary-Acidic-Protein (GFAP) in Human Serum-Matrix with a Label-Free Impedimetric Immunosensor
by Goksu Ozcelikay, Fariba Mollarasouli, Mehmet Altay Unal, Kıvılcım Gucuyener and Sibel A. Ozkan
Biosensors 2022, 12(12), 1165; https://doi.org/10.3390/bios12121165 - 14 Dec 2022
Cited by 5 | Viewed by 3370
Abstract
In this work, immobilizing anti-GFAP antibodies via covalent attachment onto L-cysteine/gold nanoparticles that were modified with screen-printed carbon electrodes (Anti-GFAP/L-cys/AuNps/SPCE) resulted in the development of a sensitive label-free impedance immunosensor for the detection of Glial Fibrillary Acidic Protein (GFAP). The immunosensor’s stepwise construction [...] Read more.
In this work, immobilizing anti-GFAP antibodies via covalent attachment onto L-cysteine/gold nanoparticles that were modified with screen-printed carbon electrodes (Anti-GFAP/L-cys/AuNps/SPCE) resulted in the development of a sensitive label-free impedance immunosensor for the detection of Glial Fibrillary Acidic Protein (GFAP). The immunosensor’s stepwise construction was studied using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). L-cysteine was chosen as the linker between GFAP antibodies and Au NPs/SPCE because it enables the guided and stable immobilization of GFAP antibodies, thus resulting in increased immunosensor sensitivity. As a redox probe, 5 mM of [Fe(CN)6]3−/4− was used to measure the electron–transfer resistance (Ret), which was raised by the binding of antigens to the immobilized anti-GFAP on the surface of the modified electrode. A linear correlation between Rct and GFAP concentration was achieved under optimum conditions in the range of 1.0–1000.0 pg/mL, with an extraordinarily low detection limit of 51.0 fg/mL. The suggested immunosensor was successfully used to detect the presence of GFAP in human blood serum samples, yielding good findings. As a result, the proposed platform may be utilized to monitor central nervous system injuries. Full article
(This article belongs to the Special Issue Biosensors for Earlier Diagnosis of Alzheimer’s Disease)
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<p>The Nyquist diagrams (<b>A</b>) showing GFAP immunosensors, step-by-step (a: Bare SPCE; b: AuNPs/SPCE; c: L-cys/AuNPs/SPCE;d: Anti-GFAP/L-cys/AuNPs/SPCE; e: BSA/Anti-GFAP/L-cys/AuNPs/SPCE) and cyclic voltammograms (<b>B</b>).</p> Full article ">Figure 2
<p>The optimization of the AuNPs’ modifier amount.</p> Full article ">Figure 3
<p>Effect of anti-GFAP loading with the antibody solution on R<sub>ct</sub> responses measured with the Anti-GFAP/L-cys/AuNPs/SPCE for 0 (B) and 100 (S) pg mL<sup>−1</sup> GFAP standard solutions.</p> Full article ">Figure 4
<p>Effect of the anti-GFAP incubation time with the antibody solution on the R<sub>ct</sub> responses. Measured with the Anti-GFAP/L-cys/AuNPs/SPCE for 0 (B) and 100 (S) pg mL<sup>−1</sup> GFAP standard.</p> Full article ">Figure 5
<p>Effect of BSA concentration (<b>a</b>) and incubation time with BSA (<b>b</b>) on the R<sub>ct</sub> responses measured with the Anti-GFAP/L-cys/AuNPs/SPCE for 0 (B) and 100 (S) pg mL<sup>−1</sup> GFAP standard solutions.</p> Full article ">Figure 6
<p>Optimization study of GFAP incubation time.</p> Full article ">Figure 7
<p>The analytical performance of the GFAP biosensor with the EIS results (<b>A</b>) and calibration curve, (<b>B</b>) type of signal transducer, application of nanomaterials, detection technique, type of bioreceptor, type of redox marker.</p> Full article ">Figure 8
<p>Application of the developed GFAP biosensors with the EIS results, (<b>A</b>) and the calibration curve (<b>B</b>) in the synthetic serum sample.</p> Full article ">Figure 9
<p>Selectivity of the GFAP biosensor in the presence of different biomolecules in the serum sample. ΔR<sub>ct</sub> detected after immobilization of 10 ngmL<sup>−1</sup> CRP (A), 2 mM Cholesterol (B), 2 mM Lysine (C), 2 mgmL<sup>−1</sup> Hemoglobin (D), and 100 pgmL<sup>−1</sup> GFAP (E) prepared in PBS in the presence of [Fe(CN)<sub>6</sub>]<sup>3−/4−</sup> with EIS.</p> Full article ">Scheme 1
<p>The illustration of the developed GFAP immunosensor.</p> Full article ">
28 pages, 15028 KiB  
Review
Recent Advances in Wearable Biosensors for Non-Invasive Detection of Human Lactate
by Yutong Shen, Chengkun Liu, Haijun He, Mengdi Zhang, Hao Wang, Keyu Ji, Liang Wei, Xue Mao, Runjun Sun and Fenglei Zhou
Biosensors 2022, 12(12), 1164; https://doi.org/10.3390/bios12121164 - 13 Dec 2022
Cited by 19 | Viewed by 5515
Abstract
Lactate, a crucial product of the anaerobic metabolism of carbohydrates in the human body, is of enormous significance in the diagnosis and treatment of diseases and scientific exercise management. The level of lactate in the bio-fluid is a crucial health indicator because it [...] Read more.
Lactate, a crucial product of the anaerobic metabolism of carbohydrates in the human body, is of enormous significance in the diagnosis and treatment of diseases and scientific exercise management. The level of lactate in the bio-fluid is a crucial health indicator because it is related to diseases, such as hypoxia, metabolic disorders, renal failure, heart failure, and respiratory failure. For critically ill patients and those who need to regularly control lactate levels, it is vital to develop a non-invasive wearable sensor to detect lactate levels in matrices other than blood. Due to its high sensitivity, high selectivity, low detection limit, simplicity of use, and ability to identify target molecules in the presence of interfering chemicals, biosensing is a potential analytical approach for lactate detection that has received increasing attention. Various types of wearable lactate biosensors are reviewed in this paper, along with their preparation, key properties, and commonly used flexible substrate materials including polydimethylsiloxane (PDMS), polyethylene terephthalate (PET), paper, and textiles. Key performance indicators, including sensitivity, linear detection range, and detection limit, are also compared. The challenges for future development are also summarized, along with some recommendations for the future development of lactate biosensors. Full article
(This article belongs to the Special Issue Wearable Sensing for Health Monitoring)
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<p>Biosensors for lactate detection in human biofluid. Reprinted with permission from ref. [<a href="#B16-biosensors-12-01164" class="html-bibr">16</a>]. Copyright 2021 Elsevier. Reprinted with permission from ref. [<a href="#B17-biosensors-12-01164" class="html-bibr">17</a>]. Copyright 2019 Elsevier. Reprinted with permission from ref. [<a href="#B18-biosensors-12-01164" class="html-bibr">18</a>]. Copyright 2016 WILEY-VCH. Reprinted with permission from ref. [<a href="#B19-biosensors-12-01164" class="html-bibr">19</a>]. Copyright 2020 MDPI.</p> Full article ">Figure 2
<p>Schematic of the working principles for different types of electrochemical biosensors: (<b>a</b>) Amperometry (WE: Work Electrode, RE: Reference Electrode, CE: Counter Electrode), (<b>b</b>) Potentiometry, (<b>c</b>) Conductometry. Reprinted with permission from ref. [<a href="#B46-biosensors-12-01164" class="html-bibr">46</a>]. Copyright 2019 Annual Reviews.</p> Full article ">Figure 3
<p>Preparation process and characterization of electrochemical biosensors for lactate detection: (<b>a</b>) A fully integrated wireless eyeglasses-based biosensor platform for monitoring lactate in sweat in real time. Reprinted with permission from ref. [<a href="#B40-biosensors-12-01164" class="html-bibr">40</a>]. Copyright 2017 Royal Society of Chemistry. (<b>b</b>) A wearable lactate biosensor fabricated by in-situ preparation of PB sensing membrane incorporated with rGO and urchin-like Au NPs on flexible SPCE. Reprinted with permission from ref. [<a href="#B65-biosensors-12-01164" class="html-bibr">65</a>]. Copyright 2022 Elsevier. (<b>c</b>) Electrochemical sensor for detecting lactate using the wires as substrate. Reprinted with permission from ref. [<a href="#B39-biosensors-12-01164" class="html-bibr">39</a>]. Copyright 2020 Nature Publishing Group. (<b>d</b>) Electrochemical biosensor prepared by electrospinning for detecting lactate in human sweat. Reprinted with permission from ref. [<a href="#B53-biosensors-12-01164" class="html-bibr">53</a>]. Copyright 2021 Elsevier. (<b>e</b>) Coupling of silk fibroin nanofibrils enzymatic membrane with ultra-thin PtNPs/Graphene film to acquire long and stable on-skin sweat lactate sensing. Reprinted with permission from ref. [<a href="#B72-biosensors-12-01164" class="html-bibr">72</a>]. Copyright 2021 Wiley-VCH. (<b>f</b>) Electrochemical sensor for enzymatic lactate detection based on laser-scribed graphitic carbon. Reprinted with permission from ref. [<a href="#B73-biosensors-12-01164" class="html-bibr">73</a>]. Copyright 2022 Elsevier.</p> Full article ">Figure 4
<p>Schematic of the working principles for different types of optical biosensors: (<b>a</b>) Passive, (<b>b</b>) Photoluminescence (R*: the excited state species), (<b>c</b>) Electroluminescence (A*: the excited state). Reprinted with permission from ref. [<a href="#B86-biosensors-12-01164" class="html-bibr">86</a>]. Copyright 2007 Elsevier.</p> Full article ">Figure 5
<p>Preparation process and characterization of optical biosensors for lactate detection: (<b>a</b>) A soft, flexible, and stretchable microfluidic system for colorimetric analysis of lactate concentration. Reprinted with permission from ref. [<a href="#B102-biosensors-12-01164" class="html-bibr">102</a>]. Copyright 2016 American Association for the Advancement of Science. (<b>b</b>) An electrogenerated chemiluminescent biosensor based on a g-C<sub>3</sub>N<sub>4</sub>-hemin nanocomposite and HGNPs for the detection of lactate. (A) ECL responses of the biosensor to lactate with different concentrations. (B) The curve of the linear relationship between ECL signal intensity and the concentration of lactate. Reprinted with permission from ref. [<a href="#B105-biosensors-12-01164" class="html-bibr">105</a>]. Copyright 2014 Royal Society of Chemistry. (<b>c</b>) A flexible MIP-ECL sensor for epidermal analyte detection. (A) The synthesis of Ru-PEI@SiO<sub>2</sub>. (B) The fabrication of a flexible MIP-ECL sensor. (i) Galvanic conversion of Ag NWs/PDMS to an Au NT/PDMS electrode. (ii) Immobilization of HLNs on an Au NT electrode. (iii) UV-vis light-induced polymerization to form a target-imprinted MIP layer on HLNs/Au NTs. (iv) Elution of flexible MIP-ECL sensors. (v) Epidermal analyte detection. Reprinted with permission from ref. [<a href="#B106-biosensors-12-01164" class="html-bibr">106</a>]. Copyright 2019 Royal Society of Chemistry. (<b>d</b>) A wearable permeable sweat sampling patch for sweat lactate detection. Reprinted with permission from ref. [<a href="#B109-biosensors-12-01164" class="html-bibr">109</a>]. Copyright 2021 American Chemical Society. (<b>e</b>) A textile-based microfluidic device integrated SERS technology and colorimetric assay as a multifunctional sweat sensor. Reprinted with permission from ref. [<a href="#B113-biosensors-12-01164" class="html-bibr">113</a>]. Copyright 2022 Elsevier. (<b>f</b>) A novel 3D titania dioxide nanotube/alginate hydrogel scaffold used to detect lactate in sweat. Reprinted with permission from ref. [<a href="#B114-biosensors-12-01164" class="html-bibr">114</a>]. Copyright 2021 American Chemical Society.</p> Full article ">Figure 6
<p>Preparation process and characterization of semiconductor biosensors for lactate detection: (<b>a</b>) Nickel oxide thin-film field-effect transistor based on radio frequency. Reprinted with permission from ref. [<a href="#B117-biosensors-12-01164" class="html-bibr">117</a>]. Copyright 2017 Elsevier. (<b>b</b>) An extended-gate type OFET for lactate detection in aqueous media. Reprinted with permission from ref. [<a href="#B121-biosensors-12-01164" class="html-bibr">121</a>]. Copyright 2015 Elsevier. (<b>c</b>) An organic voltage amplifier for lactate sensor on flexible plastic foil. Reprinted with permission from ref. [<a href="#B123-biosensors-12-01164" class="html-bibr">123</a>]. Copyright 2020 WILEY-VCH. (<b>d</b>) OECT used as highly sensitive lactate sensors by modifying the gate electrode with LOx and poly(n-vinyl-2-pyrrolidone) -capped Pt NPs. (A) Schematic diagram of lactate sensor based on OECT integrated with microfluidic channel (LOx solution was used instead of GOx solution). (B) Gate electrode modification of device. (C) Transfer curve and corresponding transconductance curve of an OECT. (D) Output curve of OECT. Reprinted with permission from ref. [<a href="#B128-biosensors-12-01164" class="html-bibr">128</a>]. Copyright 2016 WILEY-VCH. (<b>e</b>) A cumulative mode OECT prepared using n-type polymers. Reprinted with permission from ref. [<a href="#B131-biosensors-12-01164" class="html-bibr">131</a>]. Copyright 2018 American Association for the Advancement of Science. (<b>f</b>) FECTs based on multi-walled carbon nanotube and PPy composites for noninvasive lactate sensing. Reprinted with permission from ref. [<a href="#B132-biosensors-12-01164" class="html-bibr">132</a>]. Copyright 2020 Springer. (<b>g</b>) An organically modified sol-gel solid electrolyte for printed OECT-based lactate biosensor. Reprinted with permission from ref. [<a href="#B134-biosensors-12-01164" class="html-bibr">134</a>]. Copyright 2015 Springer.</p> Full article ">Figure 7
<p>Preparation process and characterization of self-powered biosensors for lactate detection: (<b>a</b>) A self-powered piezoelectric biosensor based on enzyme/ZnO nanoarrays. (A) The fabrication process of the electronic skin. (B) The piezoelectric impulse of the piezoelectric biosensor. (C) The piezoelectric output voltage and response of the piezoelectric biosensor in different concentration of lactate. (D) The detection limit and the resolution of the piezoelectric lactate biosensor. (E) Optical images of the electronic skin. (F) The electronic skin for detecting lactate. Reprinted with permission from ref. [<a href="#B135-biosensors-12-01164" class="html-bibr">135</a>]. Copyright 2017 American Chemical Society. (<b>b</b>) A self-powered piezoelectric biosensing textiles based on PVDF/T-ZnO. Reprinted with permission from ref. [<a href="#B136-biosensors-12-01164" class="html-bibr">136</a>]. Copyright 2019 MDPI. (<b>c</b>) MFC as a self-powered lactate sensor that can be used to monitor sweat lactate. Reprinted with permission from ref. [<a href="#B144-biosensors-12-01164" class="html-bibr">144</a>]. Copyright 2019 IEEE Proceedings. (<b>d</b>) A self-powered lactate biosensor fabricated from porous carbon film (modified with LOx). Reprinted with permission from ref. [<a href="#B145-biosensors-12-01164" class="html-bibr">145</a>]. Copyright 2019 Elsevier.</p> Full article ">Figure 8
<p>Biosensors for lactate detection based on different substrate materials: (<b>a</b>–<b>e</b>) Biosensors for detecting lactate based on PDMS. Reprinted with permission from ref. [<a href="#B55-biosensors-12-01164" class="html-bibr">55</a>,<a href="#B109-biosensors-12-01164" class="html-bibr">109</a>,<a href="#B149-biosensors-12-01164" class="html-bibr">149</a>,<a href="#B150-biosensors-12-01164" class="html-bibr">150</a>,<a href="#B152-biosensors-12-01164" class="html-bibr">152</a>]. Copyright 2019 IEEE Proceedings. Copyright 2017 American Chemical Society. Copyright 2021 American Chemical Society. Copyright 2021 Elsevier. Copyright 2021 Wiley-VCH. (<b>f</b>–<b>h</b>) Biosensors for detecting lactate based on PET. Reprinted with permission from ref. [<a href="#B38-biosensors-12-01164" class="html-bibr">38</a>,<a href="#B75-biosensors-12-01164" class="html-bibr">75</a>,<a href="#B153-biosensors-12-01164" class="html-bibr">153</a>]. Copyright 2014 Royal Society of Chemistry. Copyright 2020 IEEE Sensor Journal. Copyright 2019 Advancement of Science. (<b>i</b>,<b>j</b>) Biosensors for detecting lactate based on paper. Reprinted with permission from ref. [<a href="#B30-biosensors-12-01164" class="html-bibr">30</a>,<a href="#B155-biosensors-12-01164" class="html-bibr">155</a>]. Copyright 2021 Elsevier. Copyright 2021 MDPI. (<b>k</b>,<b>l</b>) Biosensors for detecting lactate based on fabric. Reprinted with permission from ref. [<a href="#B76-biosensors-12-01164" class="html-bibr">76</a>,<a href="#B157-biosensors-12-01164" class="html-bibr">157</a>]. Copyright 2021 Elsevier. Copyright 2022 Elsevier.</p> Full article ">
12 pages, 1357 KiB  
Article
A Microfluidic Platform with an Embedded Miniaturized Electrochemical Sensor for On-Chip Plasma Extraction Followed by In Situ High-Sensitivity C-Reactive Protein (hs-CRP) Detection
by Zhi-Xuan Lai, Chia-Chien Wu and Nien-Tsu Huang
Biosensors 2022, 12(12), 1163; https://doi.org/10.3390/bios12121163 - 13 Dec 2022
Cited by 6 | Viewed by 2787
Abstract
Blood testing is a clinical diagnostic tool to evaluate physiological conditions, the immune system response, or the presence of infection from whole blood samples. Although conventional blood testing can provide rich biological information, it usually requires complicated and tedious whole blood processing steps [...] Read more.
Blood testing is a clinical diagnostic tool to evaluate physiological conditions, the immune system response, or the presence of infection from whole blood samples. Although conventional blood testing can provide rich biological information, it usually requires complicated and tedious whole blood processing steps operated by benchtop instruments and well-experienced technicians, limiting its usage in point-of-care (POC) settings. To address the above problems, we propose a microfluidic platform for on-chip plasma extraction directly from whole blood and in situ biomarker detection. Herein, we chose C-reactive protein (CRP) as the target biomarker, which can be used to predict fatal cardiovascular disease (CVD) events such as heart attacks and strokes. To achieve a rapid, undiluted, and high-purity on-chip plasma extraction, we combined two whole blood processing methods: (1) anti-D immunoglobulin-assisted sedimentation, and (2) membrane filtration. To perform in situ CRP detection, we fabricated a three-dimensional (3D) microchannel with an embedded electrochemical (EC) sensor, which has a modular design to attach the blood collector and buffer reservoir with standard Luer connectors. As a proof of concept, we first confirmed that the dual plasma extraction design achieved the same purity level as the standard centrifugation method with smaller sample (100 µL of plasma extracted from 400 µL of whole blood) and time (7 min) requirements. Next, we validated the functionalization protocol of the EC sensor, followed by evaluating the detection of CRP spiked in plasma and whole blood. Our microfluidic platform performed on-chip plasma extraction directly from whole blood and in situ CRP detection at a 0.1–10 μg/mL concentration range, covering the CVD risk evaluation level of the high-sensitivity CRP (hs-CRP) test. Based on the above features, we believe that this platform constitutes a flexible way to integrate the processing of complex samples with accurate biomarker detection in a sample-to-answer POC platform, which can be applied in CVD risk monitoring under critical clinical situations. Full article
(This article belongs to the Special Issue Biosensing for Point-of-Care Diagnostics)
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<p>(<b>A</b>) The schematic and (<b>B</b>) a photo of the microfluidic platform. The red-framed inset shows the composition of the microchannel with an embedded electrochemical (EC) sensor, connecting the blood container and the buffer syringe. (<b>C</b>) The operation protocol of the microfluidic platform: (1) buffer preloading; (2) blood loading; (3) blood sedimentation; (4) plasma extraction and incubation; (5) washing and electrochemical impedance spectroscopy (EIS) measurement. (<b>D</b>) Photos of blood loading (step 2), sedimentation (step 3), and plasma extraction (step 4) in the microfluidic platform.</p> Full article ">Figure 2
<p>(<b>A</b>) The time-lapsed normalized plasma height and (<b>B</b>) a series of photos of blood sedimentation in the blood containers with (green line) and without (blue line) anti-D treatment at 0, 7, and 15 min.</p> Full article ">Figure 3
<p>Plasma extraction performance under different whole blood processing methods: (<b>A</b>) The UV–Vis absorbance spectra of the extracted plasma processed by (1) artificially hemolyzing whole blood followed by centrifugation (red line; the optical density (OD) value is shown on the right Y-axis), (2) sedimentation only (orange line), (3) filtration only (with 10× dilution; purple line), (4) sedimentation + filtration (blue line), and (5) standard centrifugation (black line). The light bandwidth represents the corresponding standard deviations (<span class="html-italic">n</span> = 3). (<b>B</b>) The absorbance spectra at 414 nm (A<sub>414</sub>) of the five whole blood processing methods. The inset shows the operation schematics of the sedimentation-only, filtration-only, and sedimentation + filtration cases.</p> Full article ">Figure 4
<p>(<b>A</b>) The schematic of the EC sensor functionalization protocol. It can be divided into five steps: (1) surface cleaning, (2) 11-MUA self-assembly, (3) EDC/NHS activation, (4) anti-CRP binding, and (5) BSA blocking step. (<b>B</b>) The Nyquist plots of EIS measurement at each functionalization step.</p> Full article ">Figure 5
<p>C-reactive protein (CRP) detection performance of the microfluidic platform: The Nyquist plots of EIS measurements and the calculated measured CRP concentrations (<span class="html-italic">n</span> = 3) in the detection of CRP spiked in (<b>A</b>,<b>B</b>) plasma and (<b>C</b>,<b>D</b>) whole blood samples.</p> Full article ">
22 pages, 5353 KiB  
Review
Recent Advances in Electrochemical Biosensors for Monitoring Animal Cell Function and Viability
by Kyeong-Mo Koo, Chang-Dae Kim, Fu Nan Ju, Huijung Kim, Cheol-Hwi Kim and Tae-Hyung Kim
Biosensors 2022, 12(12), 1162; https://doi.org/10.3390/bios12121162 - 13 Dec 2022
Cited by 15 | Viewed by 4804
Abstract
Redox reactions in live cells are generated by involving various redox biomolecules for maintaining cell viability and functions. These qualities have been exploited in the development of clinical monitoring, diagnostic approaches, and numerous types of biosensors. Particularly, electrochemical biosensor-based live-cell detection technologies, such [...] Read more.
Redox reactions in live cells are generated by involving various redox biomolecules for maintaining cell viability and functions. These qualities have been exploited in the development of clinical monitoring, diagnostic approaches, and numerous types of biosensors. Particularly, electrochemical biosensor-based live-cell detection technologies, such as electric cell–substrate impedance (ECIS), field-effect transistors (FETs), and potentiometric-based biosensors, are used for the electrochemical-based sensing of extracellular changes, genetic alterations, and redox reactions. In addition to the electrochemical biosensors for live-cell detection, cancer and stem cells may be immobilized on an electrode surface and evaluated electrochemically. Various nanomaterials and cell-friendly ligands are used to enhance the sensitivity of electrochemical biosensors. Here, we discuss recent advances in the use of electrochemical sensors for determining cell viability and function, which are essential for the practical application of these sensors as tools for pharmaceutical analysis and toxicity testing. We believe that this review will motivate researchers to enhance their efforts devoted to accelerating the development of electrochemical biosensors for future applications in the pharmaceutical industry and stem cell therapeutics. Full article
(This article belongs to the Special Issue Electrochemical (Bio-) Sensors in Biological Applications)
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<p>Schematic of various types of electrochemical biosensors for cancer/stem cell monitoring and their applications.</p> Full article ">Figure 2
<p>(<b>A</b>) Principle of the 3D ECMIS system for live-cell monitoring. (<b>B</b>) Cell-growth curves of 2D/3D-cultured HepG2 cells exposed to various anticancer drugs (cisplatin, taxol, and sorafenib) on a 2D/3D ECIS. Changes in the cell density and morphological characteristics of the 3D-cultured HepG2 cells analyzed by live–dead staining at 96 h. (<b>C</b>) Schematic of PEDOT:PSS OECT devices and a colored scanning electron microscopy (SEM) image of a fixated HEK 293 cell. (<b>D</b>) Detaching the cell from the OECT gate increases the low-pass frequency of the device’s transfer function from 2 kHz to greater than 10 kHz. (<b>E</b>) Strong attenuation of the spectrum with MDCK cells adhered to the OECT (black), added with EGTA (purple), removed from the surface (red), adherent cells (green), spectrum with opened gap junctions (pink), and fitted cells (blue). Reprinted with permission from [<a href="#B70-biosensors-12-01162" class="html-bibr">70</a>]. Copyright 2019, Elsevier; reprinted with permission from [<a href="#B75-biosensors-12-01162" class="html-bibr">75</a>]. Copyright 2021, Elsevier.</p> Full article ">Figure 3
<p>(<b>A</b>) A PLL@G-FET electrochemical biosensor that measures extracellular pH. (<b>B</b>) Monitoring of cancer cell metabolism. Comparing the extracellular acidification and pH of MCF-10A cells with those of MCF-7 cells. (<b>C</b>) Identification of the extracellular acidification of MCF-7 cells without drug treatment and with different drug concentrations (left panel), and pH changes with the intervention of various drug concentrations within 150 min (right panel). (<b>D</b>) A diagrammatic representation of the cell/gate nanogap interface. (<b>E</b>) Cell-coupled gate ISFET (left panel); HeLa, HepG2, and HUVEC cells were grown on the Ta<sub>2</sub>O<sub>5</sub> gate (right panel). (<b>F</b>) Changes in interfacial pH at the cell/gate nanogap are measured for each cell by using a cell-coupled gate ISFET biosensor. Reprinted with permission from [<a href="#B74-biosensors-12-01162" class="html-bibr">74</a>]. Copyright 2022, Elsevier. Reprinted with permission from [<a href="#B73-biosensors-12-01162" class="html-bibr">73</a>]. Copyright 2018, Royal Society of Chemistry.</p> Full article ">Figure 4
<p>(<b>A</b>) Schematic of a PB/Ti<sub>3</sub>C<sub>2</sub>/GCE biosensor for detecting H<sub>2</sub>O<sub>2</sub> from live HeLa cells. (<b>B</b>) Amperometric I–T response of the PB/Ti<sub>3</sub>C<sub>2</sub>/GCE biosensor with the consecutive treatment of 100 μM of H<sub>2</sub>O<sub>2</sub> (left panel). Current response of the amperometric detection for 0.2 mM of H<sub>2</sub>O<sub>2</sub> (right panel). (<b>C</b>) Cell viability of L929 cultured with different concentrations of nanomaterials for 5 h (left panel) and 48 h (right panel; * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01). (<b>D</b>) Schematic of the PEDOT:PSS platform functionalized with LiTFSI and CoPc for monitoring cells by detecting H<sub>2</sub>O<sub>2</sub> from live cells. (<b>E</b>) Calibration graph of the PPL/PDMS and PPLC/PDMS electrodes at various H<sub>2</sub>O<sub>2</sub> concentrations. (<b>F</b>) Representative fluorescent images of 16HBECs cultured on a PPLC/PDMS film stained with Calcein-AM (green) and PI (red) before and after they are stretched. (<b>G</b>) Amperometric responses detected from 16HBECs to various stretch stimuli (left panel), and 30% strain with various treatments at a potential of +0.55 V (vs. Ag/AgCl; right panel). Reprinted with permission from [<a href="#B77-biosensors-12-01162" class="html-bibr">77</a>]. Copyright 2020, Elsevier; reprinted with permission from [<a href="#B76-biosensors-12-01162" class="html-bibr">76</a>]. Copyright 2021, Royal Society of Chemistry.</p> Full article ">Figure 5
<p>(<b>A</b>) Construction of a 3D microgroove impedance sensor (MGIS) and a real image of the complete sensor with 3D cell culturing. (<b>B</b>) Real-time monitoring of the anticancer drug effects on 2D/3D lung cancer models. (<b>C</b>) Live–dead cell analyses of exposure to cisplatin at certain concentrations on day 7. (<b>D</b>) Schematic of a highly conductive gold nanostructure (HCGN) biosensor for the viability of 3D multicellular spheroids. (<b>E</b>) Microscopy images of 3D-cultured spheroids before/after treatment with curcumin at specific concentrations. (<b>F</b>) Electrochemical assessments of the toxicity of curcumin in 3D-cultured spheroids. Reprinted with permission from [<a href="#B121-biosensors-12-01162" class="html-bibr">121</a>]. Copyright 2020, Springer Nature; reprinted with permission from [<a href="#B58-biosensors-12-01162" class="html-bibr">58</a>]. Copyright 2020, Wiley Online Library.</p> Full article ">Figure 6
<p>(<b>A</b>) Non-invasive monitoring of a kidney organoid on-a-chip using an electrochemical method for the detection of its successful and label-free differentiation. (<b>B</b>) Representative time-dependent images of NPHS1s (podocytes), LTLs (proximal tubules), and PECAM1s (vascular networks) during the kidney organoid differentiation. (<b>C</b>) Differential pulse voltammetry (DPV) graph of the kidney organoid differentiation from day 7 to day 24 (black bar, cell outgrowth; red bar, kidney organoid). (<b>D</b>) Multifunctional graphene–Au hybrid nanoelectrode arrays (NEAs) for the monitoring of osteogenic differentiation by enhancing electrochemical signals. (<b>E</b>) Representative images of human mesenchymal stem cells (hMSCs; scale bar, 50 µm). (<b>F</b>) Cyclic voltammetry (CV) graph of time-dependent monitoring of hMSCs during osteogenic differentiation. Reprinted with permission from [<a href="#B67-biosensors-12-01162" class="html-bibr">67</a>]. Copyright 2022, Wiley Online Library; reprinted with permission from [<a href="#B114-biosensors-12-01162" class="html-bibr">114</a>]. Copyright 2018, Wiley Online Library.</p> Full article ">
13 pages, 2354 KiB  
Article
An Electrochemical Immunosensor Based on Carboxylated Graphene/SPCE for IgG-SARS-CoV-2 Nucleocapsid Determination
by Luciana de Souza Freire, Camila Macena Ruzo, Bárbara Batista Salgado, Ariamna María Dip Gandarilla, Yonny Romaguera-Barcelay, Ana P. M. Tavares, Maria Goreti Ferreira Sales, Isabelle Cordeiro, Jaila Dias Borges Lalwani, Robert Matos, Henrique Fonseca Filho, Spartaco Astolfi-Filho, Ştefan Ţălu, Pritesh Lalwani and Walter Ricardo Brito
Biosensors 2022, 12(12), 1161; https://doi.org/10.3390/bios12121161 - 13 Dec 2022
Cited by 6 | Viewed by 2886
Abstract
The COVID-19 pandemic has emphasized the importance and urgent need for rapid and accurate diagnostic tests for detecting and screening this infection. Our proposal was to develop a biosensor based on an ELISA immunoassay for monitoring antibodies against SARS-CoV-2 in human serum samples. [...] Read more.
The COVID-19 pandemic has emphasized the importance and urgent need for rapid and accurate diagnostic tests for detecting and screening this infection. Our proposal was to develop a biosensor based on an ELISA immunoassay for monitoring antibodies against SARS-CoV-2 in human serum samples. The nucleocapsid protein (N protein) from SARS-CoV-2 was employed as a specific receptor for the detection of SARS-CoV-2 nucleocapsid immunoglobulin G. N protein was immobilized on the surface of a screen-printed carbon electrode (SPCE) modified with carboxylated graphene (CG). The percentage of IgG-SARS-CoV-2 nucleocapsid present was quantified using a secondary antibody labeled with horseradish peroxidase (HRP) (anti-IgG-HRP) catalyzed using 3,3′,5,5′-tetramethylbenzidine (TMB) mediator by chronoamperometry. A linear response was obtained in the range of 1:1000–1:200 v/v in phosphate buffer solution (PBS), and the detection limit calculated was 1:4947 v/v. The chronoamperometric method showed electrical signals directly proportional to antibody concentrations due to antigen-antibody (Ag-Ab) specific and stable binding reaction. Full article
(This article belongs to the Special Issue Bio-Electric and Electrochemical Biosensors for Respiratory Viruses Detection)
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<p>(<b>A</b>) Cyclic voltammograms and (<b>B</b>) EIS measurements of bare SPCE (black), CG/SPCE (blue), EDC-NHS/CG/SPCE (magenta), N protein/EDC-NHS/CG/SPCE (green), and BSA/N protein/EDC-NHS/CG/SPCE (orange), in 5 mmol L<sup>−1</sup> [Fe(CN)<sub>6</sub>]<sup>3−/4−</sup> + 0.1 mol L<sup>−1</sup> KCl.</p> Full article ">Figure 2
<p>Optimization of N protein immobilization time: (<b>A</b>) Cyclic voltammograms in 5 mmol L<sup>−1</sup> [Fe(CN)<sub>6</sub>]<sup>3−/4−</sup> + 0.1 mol L<sup>−1</sup> KCl; (<b>B</b>) plots of Ipa (µA) versus tempo (min).</p> Full article ">Figure 3
<p>Stability of the immunosensor through cyclic voltammetry in 5 mmol L<sup>−1</sup> [Fe(CN)6]<sup>3−/4−</sup> + 0.1 mol L<sup>−1</sup> KCl.</p> Full article ">Figure 4
<p>Cyclic voltammograms for SPCE (black) and CG/SPCE (blue) in TMB solution.</p> Full article ">Figure 5
<p>(<b>A</b>) Chronoamperograms recorded with PBS, negative and positive controls. Applied potential of −0.19 V vs. pseudo-Ag. (<b>B</b>) Current values on the steady-state, negative reference serum, and PBS.</p> Full article ">Figure 6
<p>(<b>A</b>) Chronoamperometric response measured for different concentrations of human serum with IgG-SARS-CoV-2; (<b>B</b>) current values registered from 10 to 50 s for different concentrations of serum human with IgG-SARS-CoV-2; calibration curve for IgG-SARS-CoV-2 determination through (<b>C</b>) CA technique and (<b>D</b>) ELISA technique. Standard error bars correspond to measurements made on three replicates of each concentration (<span class="html-italic">n</span> = 3).</p> Full article ">Scheme 1
<p>Representation of the immunosensor assembly process and mechanism of indirect detection of analyte target.</p> Full article ">Scheme 2
<p>Equivalent circuit for Nyquist plots. R<sub>S</sub>, solution resistance; CPE, constant phase element; R<sub>c</sub>, electron-transfer resistance; Z<sub>W</sub>, Warburg element due to diffusion of the redox couple ([Fe(CN)<sub>6</sub>]<sup>4−</sup>/[Fe(CN)<sub>6</sub>]<sup>3−</sup>) to the interface from the bulk interface from the bulk of the electrolyte.</p> Full article ">
14 pages, 4770 KiB  
Article
A Compact Control System to Enable Automated Operation of Microfluidic Bioanalytical Assays
by Alan M. Gonzalez-Suarez, Alexander Long, XuHai Huang and Alexander Revzin
Biosensors 2022, 12(12), 1160; https://doi.org/10.3390/bios12121160 - 13 Dec 2022
Cited by 3 | Viewed by 2946
Abstract
We describe a control system for operating valve-enabled microfluidic devices and leverage this control system to carry out a complex workflow of plasma separation from 8 μL of whole blood followed by on-chip mixing of plasma with assay reagents for biomarker detection. The [...] Read more.
We describe a control system for operating valve-enabled microfluidic devices and leverage this control system to carry out a complex workflow of plasma separation from 8 μL of whole blood followed by on-chip mixing of plasma with assay reagents for biomarker detection. The control system incorporates pumps, digital pressure sensors, a microcontroller, solenoid valves and off-the-shelf components to deliver high and low air pressure in the desired temporal sequence to meter fluid flow and actuate microvalves. Importantly, our control system is portable, which is suitable for operating the microvalve-enabled microfluidic devices in the point-of-care setting. Full article
(This article belongs to the Special Issue Advanced Microfluidic Chips and Their Applications)
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<p><b>A control system for automated operation of microfluidic devices.</b> (<b>A</b>) The control system comprises necessary pneumatic and electronic components to autonomously operate an automated microfluidic device. (<b>B</b>) Image of the microfluidic device and its functions. Negative pressure is applied to pull plasma into the device, while positive pressure is applied to flow solutions into the analysis unit and remove bubbles. Microvalves are first configured to connect analysis units in series for filling with plasma and are later reconfigured to sequester individual analysis units and commence mixing of plasma and glucose assay reagents. An analysis unit is comprised of plasma and reagent compartments (50 nL each).</p> Full article ">Figure 2
<p><b>Automated control system for microfluidic devices.</b> (<b>A</b>) Photograph of the control system describing the main components. (<b>B</b>) Setup of the microfluidic device on an inverted microscope while operated by the control system. (<b>C</b>) Diagram of component connections inside the control system and its interface with the microfluidic device.</p> Full article ">Figure 3
<p><b>Diagrams describing high- and low-pressure circuits of the control system.</b> (<b>A</b>) Diagram for the high-pressure circuit of the control system showing all components used to generate positive pressure to actuate all microvalves. The outlets 1 to 6 from the solenoid valves are connected to the microvalves in the microfluidic device. (<b>B</b>) Diagram for the low-pressure circuit to generate both positive and negative pressure to control the flow layer of the microfluidic device. The range of pressure the system provides is from −5 to 5 psig. The outlets 1 to 3 from the solenoid valves are connected to the fluid flow channels from the microfluidic device. (<b>C</b>) Microvalves actuation using the control system. Scale bar = 500 µm.</p> Full article ">Figure 4
<p><b>Plasma separation quality assessment.</b> (<b>A</b>) Schematic of the microfluidic device used for plasma separation (modified from ref. [<a href="#B14-biosensors-12-01160" class="html-bibr">14</a>]). Scale bar = 3 mm. The gray circle denotes the plasma separation membrane (PSM), and the blue channels the plasma collection microchannel. (<b>B</b>) Microfluidic device showing the PSM (dark area with dotted line) where the sample is deposited, and an empty collection microchannel before plasma extraction. (<b>C</b>) During plasma extraction, plasma travels through the PSM filling the collection microchannel, while all blood cells are retained in the PSM. Scale bar = 2 mm. (<b>D</b>) Absorbance values for plasma separated using the microfluidic device and control system compared to separation by centrifugation.</p> Full article ">Figure 5
<p><b>Using control system to perform a mix-and-read assay in a microfluidic device.</b> (<b>A</b>) A sequence of steps performed by the control system and a microfluidic device. (<b>B</b>) Schematic of the automated microfluidic device for plasma separation and biomarkers analysis. The blue channels represent fluid flow channels, while the red channels represent all microvalves. (<b>C</b>) Micrograph showing the plasma and reagent compartments before mixing. Enzymatic reaction has not yet started. (<b>D</b>) Micrograph of the same analysis unit shown after 8 min of actively mixing contents of plasma and reagent compartments. Intensity of magenta color correlates with glucose concentration in the sample. Scale bar = 500 µm.</p> Full article ">Figure 6
<p><b>Automated image analysis.</b> We used a MATLAB script to automatically analyze acquired images and generate concentration values. (<b>A</b>) Original image acquired using an inverted microscope. (<b>B</b>) The reagents chamber was detected, and its edges (blue box) and geometrical centroid (red star) were determined for analysis. (<b>C</b>) A ROI of 100 × 300 px (blue box) was created for analysis. (<b>D</b>) Each color channel was separated and analyzed individually for the posterior calculation of magenta intensity in the ROI (color boxes in each image).</p> Full article ">Figure 7
<p><b>Performing glucose detection assays in a microfluidic device operated by the control system.</b> (<b>A</b>) Images of microchambers with different concentrations of glucose. Scale bar = 500 µm. (<b>B</b>) Correlating intensity of magenta color and glucose concentration to construct a calibration curve. Limit of detection for this assay was 0.134 mM (<span class="html-italic">n</span> = 3). (<b>C</b>) Comparing levels of glucose in blood determined using a microfluidic device and a standard glucose kit (<span class="html-italic">n</span> = 3). 95% confidence intervals are plotted as grey dotted lines.</p> Full article ">
18 pages, 3452 KiB  
Article
A Fiber-Optic Sensor-Embedded and Machine Learning Assisted Smart Helmet for Multi-Variable Blunt Force Impact Sensing in Real Time
by Yiyang Zhuang, Taihao Han, Qingbo Yang, Ryan O’Malley, Aditya Kumar, Rex E. Gerald II and Jie Huang
Biosensors 2022, 12(12), 1159; https://doi.org/10.3390/bios12121159 - 13 Dec 2022
Cited by 2 | Viewed by 2497
Abstract
Early on-site diagnosis of mild traumatic brain injury (mTBI) will provide the best guidance for clinical practice. However, existing methods and sensors cannot provide sufficiently detailed physical information related to the blunt force impact. In the present work, a smart helmet with a [...] Read more.
Early on-site diagnosis of mild traumatic brain injury (mTBI) will provide the best guidance for clinical practice. However, existing methods and sensors cannot provide sufficiently detailed physical information related to the blunt force impact. In the present work, a smart helmet with a single embedded fiber Bragg grating (FBG) sensor is developed, which can monitor complex blunt force impact events in real time under both wired and wireless modes. The transient oscillatory signal “fingerprint” can specifically reflect the impact-caused physical deformation of the local helmet structure. By combination with machine learning algorithms, the unknown transient impact can be recognized quickly and accurately in terms of impact magnitude, direction, and latitude. Optimization of the training dataset was also validated, and the boosted ML models, such as the S-SVM+ and S-IBK+, are able to predict accurately with complex databases. Thus, the ML-FBG smart helmet system developed by this work may become a crucial intervention alternative during a traumatic brain injury event. Full article
(This article belongs to the Special Issue Biosensors and Neuroscience)
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<p>Schematics of the FBG sensors (<b>a</b>), the pendulum impact system (<b>b</b>) and the light pathway, as well as the signal interrogation method (<b>c</b>) used in this study.</p> Full article ">Figure 2
<p>The exemplary transient oscillatory signal generated at an impact direction of 210° and the linear correlation between the impact energy and the FBG wavelength shift. (<b>a</b>) Display of transient oscillatory signals generated at an impact direction of 210° under nine different impact kinetic energy levels ranging from 1.80 J to 19.84 J. (<b>b</b>) Linear correlation between the relative wavelength shift of the first peak (red arrow) and the applied impact energy. Data points shown in the plot are averaged values ± standard deviations. All experiments were repeated three separate times under the same condition (n = 3).</p> Full article ">Figure 3
<p>First 100 ms of the transient oscillatory signals resulting from pendulum impacts for four selected directions (0°, 90°, 180° and 270°) and seven different kinetic energy levels from 4.06 J to 17.58 J). Black arrows indicate typical spectral peaks and valleys that may serve as the “fingerprint” features for a specific impact condition.</p> Full article ">Figure 4
<p>Stacked transient oscillatory signals and the physical interpretation illustrated by three selected impact directions. (<b>a</b>) A series of FBG spectra generated using 28 different impact directions and the same initial impact energy, 19.84 J. The full view of transient signals is shown on the left. The expanded view shown in the red box to the right displays the first 30 ms of data in greater detail. The black arrows indicate three peak/valley inflection points. Orange and blue bands illustrate the “tide-like” trends of the peaks and valleys. (<b>b</b>) Depictions of physical insights for three selected impact directions at 0°, 100° and 260°. Applied forces and their directions were schematically illustrated with either the front or side views of the helmet. The FBG sensor position is pinpointed by a short blue segment, and relative FBG expansions or compressions were demonstrated schematically on the right.</p> Full article ">Figure 5
<p>First 100 ms of the transient oscillatory signals resulting from pendulum impacts for four selected directions (30°, 120°, 210° and 300°) and four impact spots with different vertical heights from 1 to 4.</p> Full article ">Figure 6
<p>Best results of the predicted data points (25% database) versus the measured data points (75% database) on the (<b>a</b>) impact magnitude, (<b>b</b>) direction and (<b>c</b>) latitude, using four ML models that include: (1) support vector machine (SVM), (2) multilayer perceptron—artificial neural network (MLP-ANN), (3) random forest (RF), and (4) IBK. The plotted data represent 25% of the parent database that was not previously included in the training process of the ML models. The dashed line represents the line of ideality, and the solid lines represent the ±10% boundaries.</p> Full article ">Figure 7
<p>Best results of the predicted data points (25% database) versus the measured data points (75% database) on the (<b>a</b>) impact magnitude, (<b>b</b>,<b>c</b>) direction, (<b>d</b>,<b>e</b>) latitude, using the boosted ML models that include: (1) SVM+, (2) S-SVM, (3) S-SVM+, (4) IBK+, (5) S-IBK, and (6) S-IBK+. The plotted data represent 25% of the parent database that was not previously included in the training process of the ML models. The dashed line represents the line of ideality, and the solid lines represent ±10% boundaries.</p> Full article ">Figure 8
<p>First 200 ms of the transient oscillatory signals resulting from pendulum impacts on the wireless smart helmet using nine different directions (−120°, −90°, −60°, −30°, 0°, 30°, 60°, 90°, and 120°) and five different kinetic energy levels (1.80 J, 6.31 J, 10.82 J, 15.33 J and 19.84 J). All corresponding transients were synchronized. The black line in each plot indicates the estimated time point from which the regularity of the signal started to dissipate. Yellow arrows and labels in the central photograph indicate the applied impact directions.</p> Full article ">Figure 9
<p>Best results of the predicted data points (25% database) versus the measured data points (75% database) on impact magnitude (<b>a</b>) and impact direction (<b>b</b>,<b>c</b>) under the wireless sensing mode. The plotted data represent 25% of the parent database that was not previously included in the training process of the ML models. The dashed line represents the line of ideality, and the solid lines represent ±10% boundaries.</p> Full article ">
16 pages, 4383 KiB  
Review
Research Progress in the Synthesis of Carbon Dots and Their Application in Food Analysis
by Yuan Yu, Lili Zhang, Xin Gao, Yuanmiao Feng, Hongyuan Wang, Caihong Lei, Yanhong Yan and Shuiping Liu
Biosensors 2022, 12(12), 1158; https://doi.org/10.3390/bios12121158 - 12 Dec 2022
Cited by 5 | Viewed by 3006
Abstract
Food safety is connected to public health, making it crucial to protecting people’s health. Food analysis and detection can assure food quality and effectively reduce the entry of harmful foods into the market. Carbon dots (CDs) are an excellent choice for food analysis [...] Read more.
Food safety is connected to public health, making it crucial to protecting people’s health. Food analysis and detection can assure food quality and effectively reduce the entry of harmful foods into the market. Carbon dots (CDs) are an excellent choice for food analysis and detection attributable to their advantages of good optical properties, water solubility, high chemical stability, easy functionalization, excellent bleaching resistance, low toxicity, and good biocompatibility. This paper focuses on the optical properties, synthesis methods, and applications of CDs in food analysis and detection, including the recent advances in food nutritional composition analysis and food quality detection, such as food additives, heavy metal ions, foodborne pathogens, harmful organic pollutants, and pH value. Moreover, this review also discusses the potentially toxic effects, current challenges, and prospects of CDs in basic research and applications. We hope that this review can provide valuable information to lay a foundation for subsequent research on CDs and promote the exploration of CDs-based sensing for future food detection. Full article
(This article belongs to the Special Issue Nanomaterial-Based Biosensors for Food Analysis)
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<p>(<b>a</b>) Schematic illustration of CDs synthesis by arc discharge. Reprinted with the permission from Ref. [<a href="#B29-biosensors-12-01158" class="html-bibr">29</a>]. Copyright 2022, AIP Publishing. (<b>b</b>) Schematic illustration of CDs synthesis and the detection strategies for PPI and ALP activity based on the aggregation and disaggregation of the CDs. Reprinted with the permission from Ref. [<a href="#B38-biosensors-12-01158" class="html-bibr">38</a>]. Copyright 2022, Elsevier.</p> Full article ">Figure 2
<p>(<b>a</b>) One-pot synthesis and purification for full-color CDs, (<b>b</b>) normalized PL spectra under optimal excitations (the colors of the lines correspond to the fluorescent color of the CDs), and (<b>c</b>) photographs under a 395 nm UV lamp. Reprinted with the permission from Ref. [<a href="#B48-biosensors-12-01158" class="html-bibr">48</a>]. Copyright 2021, ACS.</p> Full article ">Figure 3
<p>(<b>a</b>) Polarized SPC-ECL mechanism of CDs; (<b>b</b>) schematic illustration of polarized ECL sensor. Reprinted with the permission from Ref. [<a href="#B54-biosensors-12-01158" class="html-bibr">54</a>]. Copyright 2020, ACS.</p> Full article ">Figure 4
<p>Schematic illustration of the N, S-CDs/Fe<sup>3+</sup> system for detection of AA. Reprinted with the permission from Ref. [<a href="#B74-biosensors-12-01158" class="html-bibr">74</a>]. Copyright 2022, Elsevier.</p> Full article ">Figure 5
<p>Preparation strategy of fluorescent TPA-SQDS and schematic diagram for TZ detection. Reprinted with the permission from Ref. [<a href="#B76-biosensors-12-01158" class="html-bibr">76</a>]. Copyright 2022, Elsevier.</p> Full article ">Figure 6
<p>(<b>a</b>) Step I: development of a Au@CQDs-based fluorescence method for melamine detection. (<b>b</b>) Step II: detection of milk adulterated by melamine. Reprinted with the permission from Ref. [<a href="#B79-biosensors-12-01158" class="html-bibr">79</a>]. Copyright 2018, Elsevier.</p> Full article ">Figure 7
<p>Schematic diagrams showing (<b>a</b>) the synthesis of CDs and (<b>b</b>) the sensing mechanisms of CDs for Fe<sup>3+</sup>and ampicillin. Reprinted with the permission from Ref. [<a href="#B99-biosensors-12-01158" class="html-bibr">99</a>]. Copyright 2022, Elsevier.</p> Full article ">
12 pages, 2272 KiB  
Article
Molecularly Imprinted Polymer-Based Electrochemical Sensor for Rapid and Selective Detection of Hypoxanthine
by Diksha Garg, Neelam Verma and Monika
Biosensors 2022, 12(12), 1157; https://doi.org/10.3390/bios12121157 - 12 Dec 2022
Cited by 3 | Viewed by 2758
Abstract
In this paper, we report on the coupling of an electrochemical transducer with a specifically designed biomimetic and synthetic polymeric layer that serves as a recognition surface that demonstrates the molecular memory necessary to facilitate the stable and selective identification of the meat-freshness [...] Read more.
In this paper, we report on the coupling of an electrochemical transducer with a specifically designed biomimetic and synthetic polymeric layer that serves as a recognition surface that demonstrates the molecular memory necessary to facilitate the stable and selective identification of the meat-freshness indicator hypoxanthine. Consumer preferences and the food safety of meat products are largely influenced by their freshness, so it is crucial to monitor it so as to quickly identify when it deteriorates. The sensor consists of a glassy-carbon electrode, which can be regenerated in situ continuously, functionalized with molecularly imprinted polymers (MIPs) and a nanocomposite of curcumin-coated iron oxide magnetic nanospheres (C-IO-MNSs) and multiwalled carbon nanotubes (MWCNTs) that enhance the surface area as well as the electroactive characteristics. The electrochemical behavior of the fabricated sensor was analyzed by both cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Differential pulse voltammetric studies revealed the rapid response of the proposed sol-gel-MIP/MWCNT/C-IO-MNS/GCE sensor to hypoxanthine in a concentration range of 2–50 µg/mL with a lower limit of detection at 0.165 μg/mL. Application of the newly fabricated sensor demonstrated acceptable recoveries and satisfactory accuracy when used to measure hypoxanthine in different meat samples. Full article
(This article belongs to the Special Issue Role of Electrochemical Biosensors within Sustainable Food Chains and Food Safety)
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<p>Schematic illustration of the MIP-sol-gel/MWCNT/C-IO-MNS/GC electrode fabrication. MIP: molecularly imprinted polymers, MWCNT: multi walled carbon nanotubes, C-IO-MNS: curcumin coated magnetic nanospheres, GC: glassy carbon electrode.</p> Full article ">Figure 2
<p>Surface morphology of the modified electrode with (<b>a</b>) curcumin-coated iron oxide magnetic nanospheres; (<b>b</b>) multiwalled carbon nanotubes; (<b>c</b>) sol-gel MIP before and (<b>d</b>) after template extraction by scanning electron microscopy (SEM).</p> Full article ">Figure 3
<p>FTIR spectra of curcumin-coated iron oxide magnetic nanospheres (C-IO-MNSs).</p> Full article ">Figure 4
<p>Electrochemical characterization by CV (<b>a</b>) and EIS (<b>b</b>) of bare GCE (A), C-IO-MNS/GCE (B), MWCNT/C-IO-MNS/GCE (C), sol-gel MIP/MWCNT/C-IO-MNS/GCE before (D) and after (E) template extraction, and NIP/MWCNT/C-IO MNS/GCE (F) recorded in K<sub>3</sub>[Fe(CN)<sub>6</sub>] (2.5 mM), containing 10 mM KCl and 10 mM PBS (pH = 7.0); impact of pH (<b>c</b>) and sample incubation time (<b>d</b>) on the functioning of the modified electrode. CV: cyclic voltammetry, EIS: electrochemical impedance spectroscopy, C-IO-MNS: curcumin coated magnetic nanospheres, MWCNT: multi walled carbon nanotubes, GCE: glassy carbon electrode, MIP: molecularly imprinted polymers, and NIP: non-imprinted polymers.</p> Full article ">Figure 5
<p>(<b>a</b>) DPV response of sol-gel MIP/MWCNT/C-IO-MNS/GCE (i) before (blank DPV) and (ii–ix) after addition of hypoxanthine in the concentration range of 2–50 µg/mL; (<b>b</b>) Corresponding calibration curve of the same hypoxanthine concentration range; (<b>c</b>) Selective recognition of hypoxanthine with the C-IO-MNS/MWCNT/sol-gel-MIP sensor among its analogues; (<b>d</b>) DPV voltammograms of modified electrode after elution with buffer and in situ regeneration. DPV: differential pulse voltammetry.</p> Full article ">Figure 6
<p>Storage stability of proposed C-IO-MNS/cMWCNT/sol–gel-MIP/GCE sensor.</p> Full article ">
13 pages, 2285 KiB  
Article
3D Printed Voltammetric Sensor Modified with an Fe(III)-Cluster for the Enzyme-Free Determination of Glucose in Sweat
by Eleni Koukouviti, Alexios K. Plessas, Anastasios Economou, Nikolaos Thomaidis, Giannis S. Papaefstathiou and Christos Kokkinos
Biosensors 2022, 12(12), 1156; https://doi.org/10.3390/bios12121156 - 11 Dec 2022
Cited by 4 | Viewed by 2330
Abstract
In this work, a 3D printed sensor modified with a water-stable complex of Fe(III) basic benzoate is presented for the voltammetric detection of glucose (GLU) in acidic epidermal skin conditions. The GLU sensor was produced by the drop-casting of Fe(III)-cluster ethanolic mixture on [...] Read more.
In this work, a 3D printed sensor modified with a water-stable complex of Fe(III) basic benzoate is presented for the voltammetric detection of glucose (GLU) in acidic epidermal skin conditions. The GLU sensor was produced by the drop-casting of Fe(III)-cluster ethanolic mixture on the surface of a 3D printed electrode fabricated by a carbon black loaded polylactic acid filament. The oxidation of GLU was electrocatalyzed by Fe(III), which was electrochemically generated in-situ by the Fe(III)-cluster precursor. The GLU determination was carried out by differential pulse voltammetry without the interference from common electroactive metabolites presented in sweat (such as urea, uric acid, and lactic acid), offering a limit of detection of 4.3 μmol L−1. The exceptional electrochemical performance of [Fe3O(PhCO2)6(H2O)3]∙PhCO2 combined with 3D printing technology forms an innovative and low-cost enzyme-free sensor suitable for noninvasive applications, opening the way for integrated 3D printed wearable biodevices. Full article
(This article belongs to the Special Issue Printed Electrochemical Biosensors)
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<p>The final plot of the Rietveld refinement, showing the experimental, simulated (reverted) and difference powder diffraction patterns of [Fe<sub>3</sub>O(PhCO<sub>2</sub>)<sub>6</sub>(H<sub>2</sub>O)<sub>3</sub>]∙PhCO<sub>2</sub>. Vertical markers refer to the calculated positions of the Bragg reflections (<a href="#app1-biosensors-12-01156" class="html-app">Table S4</a>).</p> Full article ">Figure 2
<p>Structure of [Fe<sub>3</sub>O(PhCO<sub>2</sub>)<sub>6</sub>(H<sub>2</sub>O)<sub>3</sub>]∙PhCO<sub>2</sub> solved by powder X-ray diffraction. Colour code: Fe: orange, C: grey, H: turquoise, O: red.</p> Full article ">Figure 3
<p>(<b>A</b>) Schematic illustration of the 3D printing process of the sensor. (<b>B</b>) A photograph of the 3D printed sensor and its dimensions in cm. (<b>C</b>) Schematic illustration of the drop-casting procedure for the construction of the GLU 3DPE modified with [Fe<sub>3</sub>O(PhCO<sub>2</sub>)<sub>6</sub>(H<sub>2</sub>O)<sub>3</sub>]∙PhCO<sub>2</sub>.</p> Full article ">Figure 4
<p>DPV responses of Fe<sub>3</sub>O(PhCO<sub>2</sub>)<sub>6</sub>(H<sub>2</sub>O)<sub>3</sub>]∙PhCO<sub>2</sub>/3DPE and Fe<sub>3</sub>O<sub>2</sub>/3DPE towards 200 µmol L<sup>−1</sup> GLU in 0.1 mol L<sup>−1</sup> PB (pH 4). The 3D printed sensor was modified with 6% <span class="html-italic">w</span>/<span class="html-italic">v</span> Fe<sub>3</sub>O(PhCO<sub>2</sub>)<sub>6</sub>(H<sub>2</sub>O)<sub>3</sub>]∙PhCO<sub>2</sub> (blue and black lines) and with 6% <span class="html-italic">w</span>/<span class="html-italic">v</span> Fe<sub>3</sub>O<sub>2</sub> (red and green lines) (both modifiers as ethanolic mixtures). The reduction potential was −1.4 V for 360 s for Fe(III)-Cluster, while the reduction time was 0 s in cases of iron oxide.</p> Full article ">Figure 5
<p>Effect of the concentration of the Fe<sub>3</sub>O(PhCO<sub>2</sub>)<sub>6</sub>(H<sub>2</sub>O)<sub>3</sub>]∙PhCO<sub>2</sub> at the 3DPE on the DPV peak current values of 200 µmol L<sup>−1</sup> GLU in 0.1 mol L<sup>−1</sup> PB (pH 4) and the respective DPV responses (from down to up 2, 4, 6, 8% (<span class="html-italic">w</span>/<span class="html-italic">v</span>)). Each bar is the mean value ± SD (<span class="html-italic">n</span> = 3). Reduction of Fe(III)-cluster at −1.4 V for 360 s.</p> Full article ">Figure 6
<p>(<b>A</b>) Effect of the reduction time on the DPV peak current values of 250 µmol L<sup>−1</sup> GLU in 0.1 mol L<sup>−1</sup> PB (pH 4) obtained with Fe<sub>3</sub>O(PhCO<sub>2</sub>)<sub>6</sub>(H<sub>2</sub>O)<sub>3</sub>]∙PhCO<sub>2</sub>/3DPE applying a reduction potential at −1.4 V. (<b>B</b>) Effect of the reduction potential on the DPV peak currents of 250 µmol L<sup>−1</sup> GLU in 0.1 mol L<sup>−1</sup> PB (pH 4) at Fe<sub>3</sub>O(PhCO<sub>2</sub>)<sub>6</sub>(H<sub>2</sub>O)<sub>3</sub>]∙PhCO<sub>2</sub>/3DPE under 360 s reduction time. Each point is the mean value ± SD (<span class="html-italic">n</span> = 3).</p> Full article ">Figure 7
<p>Baseline-corrected DPV responses of GLU concentrations in the range 0–500 μmol L<sup>−1</sup> (from down to up: 0, 25, 50, 100, 150, 200, 250, 300, 350, 400, 500 μmol L<sup>–1</sup> GLU) in 0.1 mol L<sup>–1</sup> PB (pH 4) applying a reduction potential at −1.4 V for 360 s. Each point in the calibration plots is the mean value ± SD (<span class="html-italic">n</span> = 3).</p> Full article ">Figure 8
<p>(<b>A</b>) Effect of different biomarkers presented in sweat on the DPV peak height of 200 µmol L<sup>−1</sup> GLU at the Fe<sub>3</sub>O(PhCO<sub>2</sub>)<sub>6</sub>(H<sub>2</sub>O)<sub>3</sub>]∙PhCO<sub>2</sub>/3DPE, where: (blue bar) 200 µmol L<sup>−1</sup> GLU in artificial sweat (AS); (purple bar) 200 µmol L<sup>−1</sup> GLU + 250 µmol L<sup>−1</sup> UA in AS; (green bar) 200 µmol L<sup>−1</sup> GLU + 220 mmol L<sup>−1</sup> urea in AS; (orange bar) 200 µmol L<sup>−1</sup> GLU + 55 mmol L<sup>−1</sup> lactic acid (LA) in AS; (grey bar) 200 µmol L<sup>−1</sup> GLU + 250 µmol L<sup>−1</sup> UA + 220 mmol L<sup>−1</sup> urea + 55 mmol L<sup>−1</sup> lactic acid in AS. (<b>B</b>) DPV responses and respective plot for the determination of GLU in an artificial sweat sample spiked with 120 µmol L<sup>−1</sup> GLU. Each point in the bar and in the standard addition plots is the mean value ± SD (<span class="html-italic">n</span> = 3).</p> Full article ">
23 pages, 2064 KiB  
Review
Recent Advances in Nanotechnology for the Management of Klebsiella pneumoniae–Related Infections
by Mahmood Barani, Hadis Fathizadeh, Hassan Arkaban, Davood Kalantar-Neyestanaki, Majid Reza Akbarizadeh, Abduladheem Turki Jalil and Reza Akhavan-Sigari
Biosensors 2022, 12(12), 1155; https://doi.org/10.3390/bios12121155 - 10 Dec 2022
Cited by 16 | Viewed by 3370
Abstract
Klebsiella pneumoniae is an important human pathogen that causes diseases such as urinary tract infections, pneumonia, bloodstream infections, bacteremia, and sepsis. The rise of multidrug-resistant strains has severely limited the available treatments for K. pneumoniae infections. On the other hand, K. pneumoniae activity [...] Read more.
Klebsiella pneumoniae is an important human pathogen that causes diseases such as urinary tract infections, pneumonia, bloodstream infections, bacteremia, and sepsis. The rise of multidrug-resistant strains has severely limited the available treatments for K. pneumoniae infections. On the other hand, K. pneumoniae activity (and related infections) urgently requires improved management strategies. A growing number of medical applications are using nanotechnology, which uses materials with atomic or molecular dimensions, to diagnose, eliminate, or reduce the activity of different infections. In this review, we start with the traditional treatment and detection method for K. pneumoniae and then concentrate on selected studies (2015–2022) that investigated the application of nanoparticles separately and in combination with other techniques against K. pneumoniae. Full article
(This article belongs to the Special Issue Nanomaterials and Their Applications in Sensing and Biosensing)
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<p>Schematic representation for carbapenem-resistant <span class="html-italic">K. pneumoniae</span> nanopore assay adapted from [<a href="#B70-biosensors-12-01155" class="html-bibr">70</a>], Frontiers, 2019.</p> Full article ">Figure 2
<p>Schematic representation of the colorimetric nanosensor with four types of coated AuNPs. The interactions of microorganisms and AuNPs result in color changes. In the diagram, column 1 represents the blank control, and other columns show various organisms. Rows A to D represent four coated AuNPs, adapted from [<a href="#B80-biosensors-12-01155" class="html-bibr">80</a>], ACS, 2017.</p> Full article ">Figure 3
<p>Schematic representation for microcantilever array biosensor modified with gold nanoparticles for foodborne bacteria detection, adapted from [<a href="#B86-biosensors-12-01155" class="html-bibr">86</a>], Frontiers, 2019.</p> Full article ">Figure 4
<p>Antibacterial effect of Ag:Hap-NPs (xAg = 0.3, 0.2, and 0.05) on KP, adapted from [<a href="#B111-biosensors-12-01155" class="html-bibr">111</a>], Springer, 2012.</p> Full article ">Figure 5
<p>Zones of inhibition of multidrug-resistant bacteria by conventional antibiotics and AgNPs. (<b>A</b>) <span class="html-italic">Bacillus</span>; (<b>B</b>) <span class="html-italic">Micrococcus luteus</span>; (<b>C</b>) <span class="html-italic">Staphylococcus aureus</span>; (<b>D</b>) <span class="html-italic">Enterococcus faecalis</span>; (<b>E</b>) <span class="html-italic">E. coli</span>; (<b>F</b>) <span class="html-italic">Pseudomonas aeruginosa</span>; (<b>G</b>) <span class="html-italic">Acinetobacter baumannii</span>; (<b>H</b>) <span class="html-italic">K. pneumoniae</span>. Adapted from [<a href="#B112-biosensors-12-01155" class="html-bibr">112</a>], Dove Medical Press Ltd., Macclesfield, United Kingdom, 2013.</p> Full article ">
28 pages, 9237 KiB  
Review
Optical Light Sources and Wavelengths within the Visible and Near-Infrared Range Using Photoacoustic Effects for Biomedical Applications
by Unsang Jung, Jaemyung Ryu and Hojong Choi
Biosensors 2022, 12(12), 1154; https://doi.org/10.3390/bios12121154 - 10 Dec 2022
Cited by 7 | Viewed by 2488
Abstract
The photoacoustic (PA) effect occurs when sound waves are generated by light according to the thermodynamic and optical properties of the materials; they are absorption spectroscopic techniques that can be applied to characterize materials that absorb pulse or continuous wave (CW)-modulated electromagnetic radiation. [...] Read more.
The photoacoustic (PA) effect occurs when sound waves are generated by light according to the thermodynamic and optical properties of the materials; they are absorption spectroscopic techniques that can be applied to characterize materials that absorb pulse or continuous wave (CW)-modulated electromagnetic radiation. In addition, the wavelengths and properties of the incident light significantly impact the signal-to-ratio and contrast with photoacoustic signals. In this paper, we reviewed how absorption spectroscopic research results have been used in applying actual photoacoustic effects, focusing on light sources of each wavelength. In addition, the characteristics and compositions of the light sources used for the applications were investigated and organized based on the absorption spectrum of the target materials. Therefore, we expect that this study will help researchers (who desire to study photoacoustic effects) to more efficiently approach the appropriate conditions or environments for selecting the target materials and light sources. Full article
(This article belongs to the Special Issue Advanced Optical Sensing Techniques for Applications in Biomedicine)
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<p>NO<sub>2</sub> absorption cross section (red) including the photochemical dissociation area (green) and emission spectrum (blue). Reprinted from Li et al., Photoacoustics, 2022, 100325 [<a href="#B21-biosensors-12-01154" class="html-bibr">21</a>], with permission of Elsevier.</p> Full article ">Figure 2
<p>UV–Vis spectrum of the LMCT electronic transition mechanism. Koushik et al., J. Photochem. Photobiol. Chem., 2022, 427: 113811 [<a href="#B7-biosensors-12-01154" class="html-bibr">7</a>], with permission of Elsevier.</p> Full article ">Figure 3
<p>Experimental setup for pulsed photoacoustic Doppler flow measurement. Reprinted from Brunker et al., Sci. Rep., 2016, 6, 1: 20902 [<a href="#B23-biosensors-12-01154" class="html-bibr">23</a>], with permission of Springer Nature.</p> Full article ">Figure 4
<p>Profiles of velocity measurements corresponding to signal segments. Reprinted from Brunker et al., Sci. Rep., 2016, 6, 1: 20902 [<a href="#B23-biosensors-12-01154" class="html-bibr">23</a>], with permission of Springer Nature.</p> Full article ">Figure 5
<p>OR-PAM monitored the dynamic changes of tumor vasculature in response to DC101 therapy. (<b>A</b>) Depth-encoded maximum amplitude projection (MAP) of tumor vascularity, (<b>B</b>) schematic of therapy response monitoring, and (<b>C</b>) quantification of tumor vasculature. Reprinted from Zhou et al., Photoacoustics, 2019, 15: 100143 [<a href="#B22-biosensors-12-01154" class="html-bibr">22</a>], with permission of Elsevier.</p> Full article ">Figure 6
<p>(<b>A</b>) Cross-sectional PA B-mode image of a human finger. Quantifications of (<b>A</b>) vascular movement. (<b>C</b>,<b>D</b>) Frequency responses of (<b>B</b>) and their dominant frequencies of 1.35 Hz. (<b>E</b>) Comparison of the heart rates. Reprinted from J.Ahn et al., Photoacoustics, 2022, 27: 100374 [<a href="#B24-biosensors-12-01154" class="html-bibr">24</a>], with permission of Elsevier.</p> Full article ">Figure 7
<p>(<b>A</b>) Schematic of the Vevo LAZR system setup. (<b>B</b>) Photoacoustic and (<b>D</b>) ultrasound images of scAuNP-PFH-NEs. (<b>C</b>) Photoacoustic and (<b>E</b>) ultrasound signals. Reprinted from Fernandes et al., Langmuir, 2016, 32,42: 10870–10880 [<a href="#B25-biosensors-12-01154" class="html-bibr">25</a>], with permission of the American Chemical Society.</p> Full article ">Figure 8
<p>Schematic of tunable-color OR-PAM system. Reprinted from Bui et al., Sci. Rep., 2016, 8,1: 018-20139-0 [<a href="#B26-biosensors-12-01154" class="html-bibr">26</a>], with permission of Spring Nature.</p> Full article ">Figure 9
<p>(<b>A</b>) TEM image of fabricated PB NPs. (<b>B</b>) Measured absorption spectra of PB NPs (blue) and optical absorption coefficients of blood with 90% (red) and 10% (black) oxygen saturation adapted from Prahl 37 and Kollias et al. Reprinted from Bui et al., Sci. Rep., 2016, 8,1: 018-20139-0 [<a href="#B26-biosensors-12-01154" class="html-bibr">26</a>], with permission of Spring Nature.</p> Full article ">Figure 10
<p>(<b>A</b>,<b>B</b>) Photograph and corresponding PA MAP image of blood vessels in the mouse tumor model (<b>C</b>–<b>F</b>). The wavelength and the corresponding pulse energy were as follows; (<b>B</b>,<b>D</b>) 532 nm, 0.8 μJ; (<b>E</b>) 532–712 nm (full spectrum), 1.5 μJ; (<b>F</b>) 700 ± 12.5 nm, 0.2 μJ. Reprinted from Bui et al., Sci. Rep., 2016, 8,1:018-20139-0 [<a href="#B26-biosensors-12-01154" class="html-bibr">26</a>], with permission of Spring Nature.</p> Full article ">Figure 11
<p>Schematic of the photoacoustic microscope for nanoshell imaging: Ti:Sa, Ti:sapphire. Reprinted from Li et al., J. Biomed. Opt., 2009, 14: 010507 [<a href="#B27-biosensors-12-01154" class="html-bibr">27</a>], with permission from the Optical Society of America.</p> Full article ">Figure 12
<p>In vivo non-invasive photoacoustic images of nanoshell extravasation from solid tumor vasculature; (<b>A</b>,<b>C</b>,<b>E</b>,<b>G</b>) show in vivo maximum-amplitude-projected (MAP) images acquired prior to nanoshell administration; (<b>B</b>,<b>D</b>,<b>F</b>,<b>H</b>) show in vivo B-scan images that correspond to the scanning position. Reprinted from Li et al., J. Biomed. Opt., 2009, 14: 010507 [<a href="#B27-biosensors-12-01154" class="html-bibr">27</a>], with permission from the Optical Society of America.</p> Full article ">Figure 13
<p>Optical characteristics of DNDs suspended in DI water as functions of the wavelength. (<b>A</b>) Absorption spectrum measured with an integrating sphere, and (<b>B</b>) PA spectrum. Reprinted from Zhang et al., J. Biomed. Opt., 2013, 18, 2: 026018 [<a href="#B28-biosensors-12-01154" class="html-bibr">28</a>], with permission from the Optical Society of America.</p> Full article ">Figure 14
<p>Photoacoustic images taken after injecting DNDs subcutaneously at (<b>A</b>) the back (MAP image) and (<b>B</b>) the ventral side of the thigh of the mouse (B-scan image). Reprinted from Zhang et al., J. Biomed. Opt., 2013, 18, 2: 026018 [<a href="#B28-biosensors-12-01154" class="html-bibr">28</a>], with permission from the Optical Society of America.</p> Full article ">Figure 15
<p>Extinction coefficient spectra of 0.5 mM CuS NP aqueous solution (solid line) and pure water (dotted line). The vertical line is positioned at 1064 nm. Reprinted from Ku et al., ACS Nano 2012, 6, 8: 7489–7496 [<a href="#B35-biosensors-12-01154" class="html-bibr">35</a>], with permission of the Optical Society of America.</p> Full article ">Figure 16
<p>Comparison of deep embedded objects and their photoacoustic images. Photograph of (<b>A</b>) chicken breast muscle blocks stacked, (<b>B</b>) cross-section of chicken breast muscle with copper sulfide nanoparticles; two-dimensional photoacoustic image at a depth of (<b>C</b>) ∼2.5 cm and (<b>D</b>) ∼5 cm. Reprinted from Ku et al., ACS Nano. 2012; 6, 8: 7489–7496 [<a href="#B35-biosensors-12-01154" class="html-bibr">35</a>], with permission of the Optical Society of America.</p> Full article ">Figure 17
<p>(<b>A</b>) Measured energy and width of the output pulse. (<b>B</b>) Temporal trace of the output pulse at 100 kHz. (<b>C</b>) Schematic of the Q-switched EDFL system. Reprinted from Piao et al., Appl. Phys. Lett., 2016, 108, 14: 143701 [<a href="#B37-biosensors-12-01154" class="html-bibr">37</a>], with permission of the American Institute of Physics.</p> Full article ">Figure 18
<p>(<b>A</b>) Acoustic microscope. (<b>B</b>) An optical view of a stained blood smear with the transducer. (<b>C</b>) A neutrophil is visible within the crosshairs. Reprinted from Strohm et al., Photoacoustics, 2016, 4, 1: 36-42 [<a href="#B38-biosensors-12-01154" class="html-bibr">38</a>], with permission of Elsevier.</p> Full article ">Figure 19
<p>(<b>A</b>) Optical wavelengths emitted from the 532 nm fiber-coupled laser. (<b>B</b>) The absorption spectrum of the Wright–Giemsa stain. Reprinted from Strohm et al., Photoacoustics, 2016, 4, 1: 36–42 [<a href="#B38-biosensors-12-01154" class="html-bibr">38</a>], with permission of Elsevier.</p> Full article ">Figure 20
<p>Composite photoacoustic images of the neutrophils, lymphocytes, and monocytes created by merging 532 and 600 nm photoacoustic images. Reprinted from Strohm et al., Photoacoustics, 2016, 4, 1: 36–42 [<a href="#B38-biosensors-12-01154" class="html-bibr">38</a>], with permission of Elsevier.</p> Full article ">Figure 21
<p>Multi-parametric PAM platform. (<b>A</b>) System schematic (DAQ: data acquisition). (<b>B</b>) Blow-up of the scan head boxed in (<b>A</b>). (<b>C</b>) Scanning mechanism for simultaneous acquisition of vascular anatomy, oxygen saturation, and blood flow. Reprinted from Ning et al., Opt. Lett., 2015, 40, 6: 910–913 [<a href="#B39-biosensors-12-01154" class="html-bibr">39</a>], with permission of the Optical Society of America.</p> Full article ">Figure 22
<p>Simultaneous PAM of (<b>A</b>) vascular anatomy, (<b>B</b>) oxygen saturation of hemoglobin (<sub>S</sub>O<sub>2</sub>), and (<b>C</b>) blood flow speed in a nude mouse ear in vivo. Vessel segmentation reveals dynamic changes in (<b>D</b>) vessel diameter, (<b>E</b>) <sub>S</sub>O<sub>2</sub>, and (<b>F</b>) flow speed. (<b>G</b>) Conservation of the volumetric inflow and outflow rates at each bifurcation. Reprinted from Ning et al., Opt. Lett., 2015, 40, 6: 910–913 [<a href="#B39-biosensors-12-01154" class="html-bibr">39</a>], with permission of the Optical Society of America.</p> Full article ">Figure 23
<p>(<b>A</b>) In vivo I<sub>R</sub> (570) images registered and fused with the rat atlas at the positions of bregma +1, −1.5, and −2.5 mm for stimulation-OFF (upper panels) and stimulation-ON (lower panels). (<b>B</b>) Quantitative analysis of the I<sub>R</sub> (570) signal changes in the bilateral ROIs between the stimulation-ON and -OFF conditions. Reprinted from Liao et al., Neuroimage, 2010, 52, 2: 562–570 [<a href="#B40-biosensors-12-01154" class="html-bibr">40</a>], with permission of Elsevier.</p> Full article ">Figure 24
<p>(<b>A</b>) In vivo functional ΔI<sub>F(560)</sub> (upper panels) and ΔI<sub>F(600)</sub> (lower panels) images registered and fused with the rat atlas at bregma +1, −1.5, and −2.5 mm. (<b>B</b>) Quantitative analysis of the I<sub>F(560)</sub> signal changes in the bilateral ROIs between the stimulation-ON and -OFF conditions. (<b>C</b>) Quantitative analysis of the I<sub>F(600)</sub> in the bilateral ROIs between the stimulation-ON and -OFF conditions. Reprinted from Liao et al., Neuroimage, 2010, 52, 2: 562–570 [<a href="#B40-biosensors-12-01154" class="html-bibr">40</a>], with permission from Elsevier.</p> Full article ">Figure 25
<p>Scheme of the hybrid imaging system. Reprinted from Tservelakis et al., J. Microsc., 2016, 263, 3; 300–306 [<a href="#B44-biosensors-12-01154" class="html-bibr">44</a>], with permission of Wiley Online Library.</p> Full article ">Figure 26
<p>Bimodal photoacoustic and fluorescence imaging of a young rose leaf. (<b>A</b>) OR-PAM MAP image depicting the anthocyanin accumulation. (<b>B</b>) Chlorophylls autofluorescence image of the same region. (<b>C</b>) Combined image of the two contrast modes. Reprinted from Tservelakis et al., J. Microsc., 2016, 263, 3; 300–306 [<a href="#B44-biosensors-12-01154" class="html-bibr">44</a>], with permission of Wiley Online Library.</p> Full article ">Figure 27
<p>The image reconstruction process of proposed hybrid multi-wavelength PA imaging (hPAI) system. (<b>A</b>) Transmitted signal pattern, including two excitation pulses before and after CW laser illumination. (<b>B</b>) Received PA signals. (<b>C</b>) Image reconstruction process, including PA imaging before and after CW laser heating and hPAI obtained by applying differential imaging. Reprinted from Duan et al., Opt. Lett., 2018, 43, 22; 5611–5614 [<a href="#B45-biosensors-12-01154" class="html-bibr">45</a>], with permission from the Optical Society of America.</p> Full article ">Figure 28
<p>PA spectra of various chemical bond vibrations. (<b>A</b>) Absorption spectra of whole blood and pure water. (<b>B</b>) PA spectra of polyethylene and trimethylpentane. (<b>C</b>) PA spectra of water and deuterium oxide. Adapted from Wang et al., J. Biophotonics, 2012. Reprinted from Wang et al., J. Phys. Chem. Lett., 2013, 4, 13; 2177–2185 [<a href="#B46-biosensors-12-01154" class="html-bibr">46</a>], with permission of the American Chemical Society.</p> Full article ">Figure 29
<p>A phantom study that evaluates the effect of water on PA imaging in the near-infrared region. (<b>A</b>) PA spectra of PE at different water layer thicknesses. The inset shows a schematic of the constructed phantom. PE: polyethylene. (<b>B</b>) PA amplitude ratio between the first and second overtone excitation as a function of the water layer thickness. Adapted from Wang et al., J. Biophotonics, 2012. Reprinted from Wang et al., J. Phys. Chem. Lett., 2013, 4, 13; 2177–2185 [<a href="#B46-biosensors-12-01154" class="html-bibr">46</a>], with permission of the American Chemical Society.</p> Full article ">
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