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Glucose Sensors: Revolution in Diabetes Management 2016

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Chemical Sensors".

Deadline for manuscript submissions: closed (15 October 2016) | Viewed by 186685

Special Issue Editors


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Guest Editor
Department of Information Engineering, University of Padova, Padova, Italy
Interests: sensors and algorithms for continuous glucose monitoring; deconvolution and parameter estimation techniques for the study of physiological systems; linear and nonlinear biological time-series analysis; measurement and processing of biomedical signals (EEG, event-related potentials, local field potentials, fNIRS, etc.) for clinical research and applications
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, 35131 Padova PD, Italy
Interests: signal processing and classification of biomedical signals; algorithms and software to improve both performance and usability of continuous glucose monitoring (CGM) sensors; statistical methods and machine learning techniques to analyze big data in medicine
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Endocrinology Academic Medical Center at the University of Amsterdam Meibergdreef 9 1100 DD Amsterdam, The Netherlands
Interests: Diabetes mellitus, with a focus on diabetes technology, hospital normoglycaemia, insulin and glp-1 based therapies

Special Issue Information

Dear Colleagues,

Diabetes is one of the most challenging socio-health emergencies of the third millennium.  About 350 million people worldwide are estimated to be diabetic (50% of whom are undiagnosed), but this number is rapidly increasing due to aging populations and sedentary lifestyles, with the prospective of exceeding 500 million cases in 2030. Every year, 1.5 million deaths can be directly attributed to diabetes. In Western countries, the economic cost of diabetes can exceed 15% of the budget of national health systems. Therefore, impact of innovative methodologies and technologies for diabetes management can be extremely high.
Most of the existing methodologies and technologies for diabetes management rely on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. The most widely used approach is self-monitoring blood glucose (SMBG), three to four times a day, through portable minimally-invasive lancing sensor devices, which exploit the glucose-oxidase enzyme, but, in the last 15 years, Continuous Glucose Monitoring (CGM) sensors have been developed which can provide measurements, in real time, with a 1–5 min sampling period and for several consecutive days/weeks. Most of CGM sensors exploit glucose-oxidase, but devices based on different principles, e.g., fluorescence or skin dielectric properties, have been also proposed.

The development of glucose sensor technologies and methodologies for the treatment of diabetes, universally accessible and easily usable from both the patients’ and physicians’ point of view, present several challenging aspects in different areas of scientific research, ranging from medicine to physics, electronics, chemistry, ergonomics, data analysis and signal processing, and software development to mention but a few. This Special Issue aims at presenting the latest technologies and methodologies developed in this interdisciplinary field of science. Topics include, but are not limited to, the following:

    - Physiology of glucose sensing
    - Technologies for glucose sensing
    - Calibration of glucose sensors
    - Online algorithms for glucose sensors (including denoising, prediction, classification)
    - E-health, m-health and personal healthcare systems applications of glucose sensors in diabetes     management
    - New populations: elderly, obese, type 2
    - Glucose sensor requirements for open-loop vs. closed-loop clinical use
    - Non-adjunctive use of continuous glucose monitoring sensors

Dr. Giovanni Sparacino
Dr. Andrea Facchinetti
Dr. J. Hans DeVries
Guest Editors

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Keywords

  • glucose sensors
  • continuous glucose monitoring sensors
  • self-monitoring blood glucose sensors
  • non-invasive glucose sensors
  • implanted glucose sensors
  • calibration of glucose sensors
  • evaluation of glucose sensors
  • validation of glucose sensors

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Published Papers (15 papers)

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1657 KiB  
Article
Use of Wearable Sensors and Biometric Variables in an Artificial Pancreas System
by Kamuran Turksoy, Colleen Monforti, Minsun Park, Garett Griffith, Laurie Quinn and Ali Cinar
Sensors 2017, 17(3), 532; https://doi.org/10.3390/s17030532 - 7 Mar 2017
Cited by 24 | Viewed by 7729
Abstract
An artificial pancreas (AP) computes the optimal insulin dose to be infused through an insulin pump in people with Type 1 Diabetes (T1D) based on information received from a continuous glucose monitoring (CGM) sensor. It has been recognized that exercise is a major [...] Read more.
An artificial pancreas (AP) computes the optimal insulin dose to be infused through an insulin pump in people with Type 1 Diabetes (T1D) based on information received from a continuous glucose monitoring (CGM) sensor. It has been recognized that exercise is a major challenge in the development of an AP system. The use of biometric physiological variables in an AP system may be beneficial for prevention of exercise-induced challenges and better glucose regulation. The goal of the present study is to find a correlation between biometric variables such as heart rate (HR), heat flux (HF), skin temperature (ST), near-body temperature (NBT), galvanic skin response (GSR), and energy expenditure (EE), 2D acceleration-mean of absolute difference (MAD) and changes in glucose concentrations during exercise via partial least squares (PLS) regression and variable importance in projection (VIP) in order to determine which variables would be most useful to include in a future artificial pancreas. PLS and VIP analyses were performed on data sets that included seven different types of exercises. Data were collected from 26 clinical experiments. Clinical results indicate ST to be the most consistently important (important for six out of seven tested exercises) variable over all different exercises tested. EE and HR are also found to be important variables over several types of exercise. We also found that the importance of GSR and NBT observed in our experiments might be related to stress and the effect of changes in environmental temperature on glucose concentrations. The use of the biometric measurements in an AP system may provide better control of glucose concentration. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>Linear regressions between biometric variables for 1 treadmill exercise session. Red colored correlation coefficients indicate statistical correlation. The diagonal figures show histograms for each biometric variable.</p>
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<p>Distribution of VIP values for each biometric variable measured during treadmill exercise sessions.</p>
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<p>Distribution of VIP values for each biometric variable measured during treadmill exercise- interval training sessions.</p>
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<p>Distribution of VIP values for each biometric variable measured during exercise stress test sessions.</p>
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<p>Distribution of VIP values for each biometric variable measured during submaximal resistance training sessions.</p>
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<p>Distribution of VIP values for each biometric variable measured during maximal resistance training sessions.</p>
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<p>Distribution of VIP values for each biometric variable measured during bike exercise sessions.</p>
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<p>Distribution of VIP values for each biometric variable measured during workout video sessions.</p>
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455 KiB  
Article
Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs)
by Daniel P. Howsmon, Faye Cameron, Nihat Baysal, Trang T. Ly, Gregory P. Forlenza, David M. Maahs, Bruce A. Buckingham, Juergen Hahn and B. Wayne Bequette
Sensors 2017, 17(1), 161; https://doi.org/10.3390/s17010161 - 15 Jan 2017
Cited by 20 | Viewed by 6780
Abstract
Reliable continuous glucose monitoring (CGM) enables a variety of advanced technology for the treatment of type 1 diabetes. In addition to artificial pancreas algorithms that use CGM to automate continuous subcutaneous insulin infusion (CSII), CGM can also inform fault detection algorithms that alert [...] Read more.
Reliable continuous glucose monitoring (CGM) enables a variety of advanced technology for the treatment of type 1 diabetes. In addition to artificial pancreas algorithms that use CGM to automate continuous subcutaneous insulin infusion (CSII), CGM can also inform fault detection algorithms that alert patients to problems in CGM or CSII. Losses in infusion set actuation (LISAs) can adversely affect clinical outcomes, resulting in hyperglycemia due to impaired insulin delivery. Prolonged hyperglycemia may lead to diabetic ketoacidosis—a serious metabolic complication in type 1 diabetes. Therefore, an algorithm for the detection of LISAs based on CGM and CSII signals was developed to improve patient safety. The LISA detection algorithm is trained retrospectively on data from 62 infusion set insertions from 20 patients. The algorithm collects glucose and insulin data, and computes relevant fault metrics over two different sliding windows; an alarm sounds when these fault metrics are exceeded. With the chosen algorithm parameters, the LISA detection strategy achieved a sensitivity of 71.8% and issued 0.28 false positives per day on the training data. Validation on two independent data sets confirmed that similar performance is seen on data that was not used for training. The developed algorithm is able to effectively alert patients to possible infusion set failures in open-loop scenarios, with limited evidence of its extension to closed-loop scenarios. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>Illustration of continuous glucose monitor (CGM) sensor fault detection based on hardware redundancy. In this example, (<b>A</b>) a patient wears two CGM sensors at different locations, and (<b>B</b>) these signals are compared to generate a residual. A potential fault detection scheme based on hardware redundancy would analyze the residual for fault signatures; in this case, a simple threshold at ±20 mg/dL was used.</p>
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<p>Illustration of the glucose fault metric (<math display="inline"> <semantics> <mi>GFM</mi> </semantics> </math>) calculation for <math display="inline"> <semantics> <mrow> <mi>L</mi> <mi>W</mi> <mo>=</mo> <mn>24</mn> </mrow> </semantics> </math> h and <math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>W</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> h. (<b>A</b>) The length of the horizontal lines corresponding to <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>CGM</mi> <mo>¯</mo> </mover> <mrow> <mi>k</mi> <mo>|</mo> <mi>k</mi> <mo>−</mo> <mi>L</mi> <mi>W</mi> </mrow> </msub> </mrow> </semantics> </math> and <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>CGM</mi> <mo>¯</mo> </mover> <mrow> <mi>k</mi> <mo>|</mo> <mi>k</mi> <mo>−</mo> <mi>S</mi> <mi>W</mi> </mrow> </msub> </mrow> </semantics> </math> indicate the length of time of long window (<math display="inline"> <semantics> <mrow> <mi>L</mi> <mi>W</mi> </mrow> </semantics> </math>) and short window (<math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>W</mi> </mrow> </semantics> </math>), respectively; (<b>B</b>) averages are computed for each new data point. The marked points correspond to the horizontal bars in the top figure; (<b>C</b>) when <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>CGM</mi> <mo>¯</mo> </mover> <mrow> <mi>k</mi> <mo>|</mo> <mi>k</mi> <mo>−</mo> <mi>S</mi> <mi>W</mi> </mrow> </msub> <mo>&lt;</mo> <msub> <mover accent="true"> <mi>CGM</mi> <mo>¯</mo> </mover> <mrow> <mi>k</mi> <mo>|</mo> <mi>k</mi> <mo>−</mo> <mi>L</mi> <mi>W</mi> </mrow> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <mi>GFM</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math>. However, when <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>CGM</mi> <mo>¯</mo> </mover> <mrow> <mi>k</mi> <mo>|</mo> <mi>k</mi> <mo>−</mo> <mi>S</mi> <mi>W</mi> </mrow> </msub> <mo>&gt;</mo> <msub> <mover accent="true"> <mi>CGM</mi> <mo>¯</mo> </mover> <mrow> <mi>k</mi> <mo>|</mo> <mi>k</mi> <mo>−</mo> <mi>L</mi> <mi>W</mi> </mrow> </msub> </mrow> </semantics> </math>, the area between these two curves accumulates in <math display="inline"> <semantics> <mi>GFM</mi> </semantics> </math>.</p>
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<p>Illustration of the insulin fault metric (<math display="inline"> <semantics> <mi>IFM</mi> </semantics> </math>) calculation for <math display="inline"> <semantics> <mrow> <mi>L</mi> <mi>W</mi> <mo>=</mo> <mn>24</mn> </mrow> </semantics> </math> h and <math display="inline"> <semantics> <mrow> <mi>S</mi> <mi>W</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math> h. (<b>A</b>) The patient’s glucose level is given over time. Time points with retrospective alarms are shaded in red; (<b>B</b>) the basal and bolus insulin administration is passed to a second-order filter to determine the plasma insulin estimate (<math display="inline"> <semantics> <mi>PIE</mi> </semantics> </math>); (<b>C</b>) the <math display="inline"> <semantics> <mi>IFM</mi> </semantics> </math> calculated from the given insulin profile.</p>
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<p>The pROC generated from the performance of different algorithm parameter sets on T1. The marker for the chosen parameter set is enlarged and highlighted in red.</p>
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888 KiB  
Article
Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges
by Andrea Facchinetti
Sensors 2016, 16(12), 2093; https://doi.org/10.3390/s16122093 - 9 Dec 2016
Cited by 127 | Viewed by 14556
Abstract
Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, [...] Read more.
Continuous glucose monitoring (CGM) sensors are portable devices that allow measuring and visualizing the glucose concentration in real time almost continuously for several days and are provided with hypo/hyperglycemic alerts and glucose trend information. CGM sensors have revolutionized Type 1 diabetes (T1D) management, improving glucose control when used adjunctively to self-monitoring blood glucose systems. Furthermore, CGM devices have stimulated the development of applications that were impossible to create without a continuous-time glucose signal, e.g., real-time predictive alerts of hypo/hyperglycemic episodes based on the prediction of future glucose concentration, automatic basal insulin attenuation methods for hypoglycemia prevention, and the artificial pancreas. However, CGM sensors’ lack of accuracy and reliability limited their usability in the clinical practice, calling upon the academic community for the development of suitable signal processing methods to improve CGM performance. The aim of this paper is to review the past and present algorithmic challenges of CGM sensors, to show how they have been tackled by our research group, and to identify the possible future ones. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>The accuracy timeline of CGM sensors over the last 15 years.</p>
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<p>The smart CGM sensor architecture, which consists of placing, in cascade to the output of a commercial CGM sensor, three software modules, able to work in real time, for denoising the random noise component, enhancing the accuracy, and predicting the future glucose concentration (adapted from [<a href="#B27-sensors-16-02093" class="html-bibr">27</a>]).</p>
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<p>The T1D-DM model developed to generate ISCT to test SMBG-based or CGM-based treatment decisions.</p>
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3522 KiB  
Article
Remote Blood Glucose Monitoring in mHealth Scenarios: A Review
by Giordano Lanzola, Eleonora Losiouk, Simone Del Favero, Andrea Facchinetti, Alfonso Galderisi, Silvana Quaglini, Lalo Magni and Claudio Cobelli
Sensors 2016, 16(12), 1983; https://doi.org/10.3390/s16121983 - 24 Nov 2016
Cited by 30 | Viewed by 9747
Abstract
Glucose concentration in the blood stream is a critical vital parameter and an effective monitoring of this quantity is crucial for diabetes treatment and intensive care management. Effective bio-sensing technology and advanced signal processing are therefore of unquestioned importance for blood glucose monitoring. [...] Read more.
Glucose concentration in the blood stream is a critical vital parameter and an effective monitoring of this quantity is crucial for diabetes treatment and intensive care management. Effective bio-sensing technology and advanced signal processing are therefore of unquestioned importance for blood glucose monitoring. Nevertheless, collecting measurements only represents part of the process as another critical task involves delivering the collected measures to the treating specialists and caregivers. These include the clinical staff, the patient’s significant other, his/her family members, and many other actors helping with the patient treatment that may be located far away from him/her. In all of these cases, a remote monitoring system, in charge of delivering the relevant information to the right player, becomes an important part of the sensing architecture. In this paper, we review how the remote monitoring architectures have evolved over time, paralleling the progress in the Information and Communication Technologies, and describe our experiences with the design of telemedicine systems for blood glucose monitoring in three medical applications. The paper ends summarizing the lessons learned through the experiences of the authors and discussing the challenges arising from a large-scale integration of sensors and actuators. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>The early architecture adopted for the remote monitoring of Type 1 Diabetes Patients. The architecture encompassed two separate units, each one hosting different services, asynchronously communicating using modems operating over landlines.</p>
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<p>The web-centric architecture emerged during the first decade of the new century. A large number of devices accessed a central server using multimodal techniques.</p>
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<p>The architecture adopted for a Body Area Network sees multiple wearable devices coordinated by a <span class="html-italic">Body Gateway</span>. The <span class="html-italic">Body Gateway</span> interacts using <span class="html-italic">Bluetooth</span> with the <span class="html-italic">Network Hub</span> that enacts remote monitoring.</p>
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<p>The architecture illustrating the components used in the final trial of <span class="html-italic">AP@home</span>, with the <span class="html-italic">Diabetes Assistant</span> acting both as a <span class="html-italic">Body Gateway</span> and as a <span class="html-italic">Network Hub</span>.</p>
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<p>The remote monitoring architecture used for the adolescents camp held in Bardonecchia. The system was used by the clinical and technical staff for safety and research purposes and was proposed to the parents as a means of managing the disease of their children.</p>
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<p>The components developed for the <span class="html-italic">Neokid</span> mobile application. (<b>a</b>) the smartphone application; (<b>b</b>) the detailed Continuous Glucose Monitoring track for a single patient; and (<b>c</b>) the panel with the suggested glucose infusion rates.</p>
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3181 KiB  
Article
A Robust, Enzyme-Free Glucose Sensor Based on Lysine-Assisted CuO Nanostructures
by Qurrat-ul-Ain Baloach, Aneela Tahira, Arfana Begum Mallah, Muhammad Ishaq Abro, Siraj Uddin, Magnus Willander and Zafar Hussain Ibupoto
Sensors 2016, 16(11), 1878; https://doi.org/10.3390/s16111878 - 14 Nov 2016
Cited by 24 | Viewed by 6180
Abstract
The production of a nanomaterial with enhanced and desirable electrocatalytic properties is of prime importance, and the commercialization of devices containing these materials is a challenging task. In this study, unique cupric oxide (CuO) nanostructures were synthesized using lysine as a soft template [...] Read more.
The production of a nanomaterial with enhanced and desirable electrocatalytic properties is of prime importance, and the commercialization of devices containing these materials is a challenging task. In this study, unique cupric oxide (CuO) nanostructures were synthesized using lysine as a soft template for the evolution of morphology via a rapid and boiled hydrothermal method. The morphology and structure of the synthesized CuO nanomaterial were characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD), respectively. The prepared CuO nanostructures showed high potential for use in the electrocatalytic oxidation of glucose in an alkaline medium. The proposed enzyme-free glucose sensor demonstrated a robust response to glucose with a wide linear range and high sensitivity, selectivity, stability, and reproducibility. To explore its practical feasibility, the glucose content of serum samples was successfully determined using the enzyme-free sensor. An analytical recovery method was used to measure the actual glucose from the serum samples, and the results were satisfactory. Moreover, the presented glucose sensor has high chemical stability and can be reused for repetitive measurements. This study introduces an enzyme-free glucose sensor as an alternative tool for clinical glucose quantification. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>XRD spectrum of CuO nanostructures prepared in the presence of lysine as a soft template.</p>
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<p>Scanning electron microscope images of CuO nanostructures at (<b>A</b>) low and (<b>B</b>) high magnification levels.</p>
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<p>Cyclic voltammograms recorded in 0.1 M NaOH in either (<b>a</b>) the absence of glucose or (<b>b</b>) the presence of 1 mM glucose with a bare GCE; and CuO-nanostructure-modified GCEs in either (<b>c</b>) the absence of glucose or (<b>d</b>) the presence of 1 mM glucose. Scan rates are 0.05 mV·s<sup>−1</sup>.</p>
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<p>(<b>A</b>) Cyclic voltammograms obtained with CuO-nanostructure-modified GCEs in 0.1 M NaOH and 1 mM glucose at various scan rates (150, 200, 300, 400, 500, 600, 700, 800, 900 and 1000 mV·s<sup>−1</sup>; (<b>B</b>) The inset shows the plot of Ip versus the square root of the scan rate.</p>
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<p>Amperometric current-time response curves for the progressive addition of 0.1 mL aliquots of 100 mM glucose into stirred 0.1 M NaOH at (<b>A</b>) a GCE modified with CuO nanostructures; (<b>B</b>) Calibration plot currents versus glucose concentrations ranging from 0.25 mM to 13.25 mM.</p>
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<p>Amperometric current-time response curves for the sequential addition of 0.1 mM aliquots of glucose, dopamine, ascorbic acid, and uric acid.</p>
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<p>(<b>A</b>) Square wave voltammograms for different glucose concentrations: 0.01 mM, 0.1 mM, 0.5 mM, 0.8 mM, 1 mM and 1.5 mM; (<b>B</b>) A calibration plot of Ip versus glucose concentration.</p>
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<p>(<b>A</b>) Differential pulse voltammograms for different glucose concentrations (0.1 mM, 0.5 mM, 1 mM, 2 mM, and 2.5 mM); (<b>B</b>) A calibration plot of Ip versus glucose concentration.</p>
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<p>The developed non-enzymatic glucose sensor response was reproducible across electrodes.</p>
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2105 KiB  
Article
Non-Enzymatic Glucose Sensing Using Carbon Quantum Dots Decorated with Copper Oxide Nanoparticles
by Houcem Maaoui, Florina Teodoresu, Qian Wang, Guo-Hui Pan, Ahmed Addad, Radhouane Chtourou, Sabine Szunerits and Rabah Boukherroub
Sensors 2016, 16(10), 1720; https://doi.org/10.3390/s16101720 - 18 Oct 2016
Cited by 45 | Viewed by 9305
Abstract
Perturbations in glucose homeostasis is critical for human health, as hyperglycemia (defining diabetes) leads to premature death caused by macrovascular and microvascular complications. However, the simple and accurate detection of glucose in the blood at low cost remains a challenging task, although it [...] Read more.
Perturbations in glucose homeostasis is critical for human health, as hyperglycemia (defining diabetes) leads to premature death caused by macrovascular and microvascular complications. However, the simple and accurate detection of glucose in the blood at low cost remains a challenging task, although it is of great importance for the diagnosis and therapy of diabetic patients. In this work, carbon quantum dots decorated with copper oxide nanostructures (CQDs/Cu2O) are prepared by a simple hydrothermal approach, and their potential for electrochemical non-enzymatic glucose sensing is evaluated. The proposed sensor exhibits excellent electrocatalytic activity towards glucose oxidation in alkaline solutions. The glucose sensor is characterized by a wide concentration range from 6 µM to 6 mM, a sensitivity of 2.9 ± 0.2 µA·µM−1·cm−2, and a detection limit of 6 µM at a signal-to-noise ratio S/N = 3. The sensors are successfully applied for glucose determination in human serum samples, demonstrating that the CQDs/Cu2O-based glucose sensor satisfies the requirements of complex sample detection with adapted potential for therapeutic diagnostics. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>Characterization of carbon quantum dots loaded with copper oxide nanoparticles (CQDs/Cu<sub>2</sub>O NPs): (<b>A</b>) X-ray powder diffraction (XRD) pattern; (<b>B</b>) transmission electron microscopy (TEM) image; (<b>C</b>) high-resolution TEM (HRTEM) image.</p>
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<p>(<b>A</b>) X-ray photoelectron spectroscopy (XPS) survey spectrum; (<b>B</b>) Cu<sub>2p</sub> core level spectrum; (<b>C</b>) C<sub>1s</sub> core level spectrum; (<b>D</b>) Raman spectrum; (<b>E</b>) thermogravimetric analysis (TGA) thermogram of CQDs/Cu<sub>2</sub>O nanocomposite.</p>
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<p>Cyclic voltammograms of (<b>A</b>) glassy carbon electrode (GCE); (<b>B</b>) GCE/Cu<sub>2</sub>O; and (<b>C</b>) GCE modified with CQDs/Cu<sub>2</sub>O by drop casting in 0.1 M NaOH aqueous solution in the absence (red) and presence of glucose (black, 100 µM).</p>
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<p>(<b>A</b>) Amperometric response curve of CQDs/Cu<sub>2</sub>O-modified GCE polarized at +0.55 V vs. Ag/AgCl upon successive additions of glucose (100 µM) in 0.1 M NaOH (up to a total of 500 µM); (<b>B</b>) Calibration curve for CQDs/Cu<sub>2</sub>O-modified GCE electrodes for the determination of glucose. The inset corresponds to a calibration curve for glucose concentrations of 0–10 µM.</p>
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<p>(<b>A</b>) Interference test of CQDs/Cu<sub>2</sub>O-modified GCE electrode in 0.1 M NaOH at +0.55 V with 100 µM glucose in the presence 500 µM dopamine (DA), uric acid (UA), ascorbic acid (AA), fructose, galactose; (<b>B</b>) current response to the addition of serum; (<b>C</b>) UV/Vis spectra of different concentrations of glucose as well as of the serum samples (ten times diluted) together with calibration curve (<b>D</b>).</p>
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3253 KiB  
Article
Mid-Infrared Photoacoustic Detection of Glucose in Human Skin: Towards Non-Invasive Diagnostics
by Jonas Kottmann, Julien M. Rey and Markus W. Sigrist
Sensors 2016, 16(10), 1663; https://doi.org/10.3390/s16101663 - 10 Oct 2016
Cited by 85 | Viewed by 13320
Abstract
Diabetes mellitus is a widespread metabolic disease without cure. Great efforts are being made to develop a non-invasive monitoring of the blood glucose level. Various attempts have been made, including a number of non-optical approaches as well as optical techniques involving visible, near- [...] Read more.
Diabetes mellitus is a widespread metabolic disease without cure. Great efforts are being made to develop a non-invasive monitoring of the blood glucose level. Various attempts have been made, including a number of non-optical approaches as well as optical techniques involving visible, near- and mid-infrared light. However, no true breakthrough has been achieved so far, i.e., there is no fully non-invasive monitoring device available. Here we present a new study based on mid-infrared spectroscopy and photoacoustic detection. We employ two setups, one with a fiber-coupled photoacoustic (PA) cell and a tunable quantum cascade laser (QCL), and a second setup with two QCLs at different wavelengths combined with PA detection. In both cases, the PA cells are in direct skin contact. The performance is tested with an oral glucose tolerance test. While the first setup often gives reasonable qualitative agreement with ordinary invasive blood glucose measurements, the dual-wavelength approach yields a considerably improved stability and an uncertainty of only ±30 mg/dL of the blood glucose concentration level at a confidence level of 90%. This result is achieved without advanced data treatment such as principal component analysis involving extended wavelength ranges. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>Schematic of the human skin with stratum corneum, epidermis, dermis and subcutaneous tissue.</p>
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<p>Dependence of the thermal diffusion length <span class="html-italic">µ</span><sub>s</sub> (<span style="color:blue">blue</span>) and the optical penetration depth <span class="html-italic">µ</span><sub>a</sub> (<span style="color:red">red</span>) on modulation frequency <span class="html-italic">f</span> for the wavelength range between 1000 and 1100 cm<sup>−1</sup>: (<b>a</b>) For water and (<b>b</b>) for the epidermis. Obviously, <span class="html-italic">µ</span><sub>a</sub> does not depend on <span class="html-italic">f</span>. The comparison between <span class="html-italic">µ</span><sub>a</sub> and <span class="html-italic">µ</span><sub>s</sub> allows a classification of different photoacoustic (PA) regimes.</p>
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<p>Experimental arrangement with two fixed-wavelength quantum cascade lasers (QCLs) and the N<sub>2</sub>-ventilated PA cell with free laser beam access. A power meter (PM) is employed for power normalization. L1, L2: lenses; RH-T sensor: Sensors for relative humidity (RH) and temperature (T), respectively; DL: diode lasers for alignment purposes.</p>
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<p>Temporal evolution of the relative humidity RH (<span style="color:blue">blue</span>) and the temperature T (<span style="color:red">red</span>) during a continuous in vivo measurement at the human forearm with the N<sub>2</sub>-ventilated open-ended PA cell.</p>
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<p>Stability of the PA signal recorded in vivo with the fiber-coupled PA sensor at the human forearm during two measurements events (<span style="color:red">red</span>) and (black). Even a short conversation with the volunteer (indicated by the arrow) increases the PA signal fluctuations.</p>
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<p>Continuously recorded “true” PA signals (<span style="color:blue">blue</span>) and invasively measured blood glucose (BG) data (<span style="color:red">dashed red</span>) during an oral glucose tolerance test (OGTT) for two individual events (<b>a</b>) and (<b>b</b>). The “true” PA signals have been obtained after smoothing the original PA data (by applying a moving average of 10 s) and by rejecting fast PA signal changes caused by movements of the volunteer’s arm. Comparison of the non-invasive PA recording (<span style="color:blue">blue</span>) with the invasive measurement (<span style="color:red">dashed red</span>) allows correlations to be found.</p>
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<p>Dependence of the PA signal on the glucose concentration (in mg/dL) in aqueous solutions for two different wavelengths 1080 cm<sup>−1</sup> (<span style="color:red">red</span>) and 1180 cm<sup>−1</sup> (<span style="color:blue">blue</span>) for a modulation frequency <span class="html-italic">f</span> of 137 Hz. The PA signals have been normalized to 1 for pure water.</p>
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<p>PA signals recorded at the fingertip of a healthy volunteer versus time with QCL 1 at 1180 cm<sup>−1</sup> (PAS1: <span style="color:blue">blue</span>), with QCL 2 at 1080 cm<sup>−1</sup> (PAS2: black), and ratio (<span style="color:#70AD47">green</span>). Despite decreasing and fluctuating individual PA measurements, the ratio exhibits a rather constant signal.</p>
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<p>Continuous PA signals at 1180 cm<sup>−1</sup> (PAS1: <span style="color:blue">blue</span>) and 1080 cm<sup>−1</sup> (PAS2: black) recorded at the fingertip of a healthy volunteer during an oral glucose tolerance test (OGTT) as well as its ratio (<span style="color:#70AD47">green</span>). A good correlation between the PA ratio signal (<span style="color:#70AD47">green</span>) and simultaneous invasive blood glucose (BG) measurements (<span style="color:red">dashed red</span>) is obtained.</p>
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<p>Correlation between invasive blood glucose data and non-invasive PA signals obtained with the 2-QCLs setup. The blue points are derived from the experimental data presented in <a href="#sensors-16-01663-f009" class="html-fig">Figure 9</a>. The red solid line shows a linear fit (with r<sup>2</sup> = 0.8), and the red dashed lines represent the confidence bounds at 90% confidence level.</p>
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1828 KiB  
Communication
A Human Serum-Based Enzyme-Free Continuous Glucose Monitoring Technique Using a Needle-Type Bio-Layer Interference Sensor
by Dongmin Seo, Sung-Ho Paek, Sangwoo Oh, Sungkyu Seo and Se-Hwan Paek
Sensors 2016, 16(10), 1581; https://doi.org/10.3390/s16101581 - 24 Sep 2016
Cited by 8 | Viewed by 6836
Abstract
The incidence of diabetes is continually increasing, and by 2030, it is expected to have increased by 69% and 20% in underdeveloped and developed countries, respectively. Therefore, glucose sensors are likely to remain in high demand in medical device markets. For the current [...] Read more.
The incidence of diabetes is continually increasing, and by 2030, it is expected to have increased by 69% and 20% in underdeveloped and developed countries, respectively. Therefore, glucose sensors are likely to remain in high demand in medical device markets. For the current study, we developed a needle-type bio-layer interference (BLI) sensor that can continuously monitor glucose levels. Using dialysis procedures, we were able to obtain hypoglycemic samples from commercial human serum. These dialysis-derived samples, alongside samples of normal human serum were used to evaluate the utility of the sensor for the detection of the clinical interest range of glucose concentrations (70–200 mg/dL), revealing high system performance for a wide glycemic state range (45–500 mg/dL). Reversibility and reproducibility were also tested over a range of time spans. Combined with existing BLI system technology, this sensor holds great promise for use as a wearable online continuous glucose monitoring system for patients in a hospital setting. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>Needle-type sensor performance and the influence of membrane size on the detection of glucose concentrations in defined buffer. (<b>a</b>) Schematic of the needle-type sensor. A syringe needle was modified and covered with a semi-permeable membrane to fabricate the needle-type sensor. Bovine serum albumin (BSA)-ligand conjugate was kept outside of the semi-permeable membrane; (<b>b</b>) A glucose sensing profile generated by the sensor. The wavelength shift increases with time as the sensor is exposed to the buffer containing the BSA-ligand conjugates, whereas the shift sharply decreases as the sensor is exposed to a solution of 500 mg/dL glucose (with BSA-ligand conjugate); (<b>c</b>) Membrane pore-size performance. The response of the sensor increased as the pore size increased from 50 nm to 200 nm. Con A: concanavalin A.</p>
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<p>Schematic of the dialysis method and the performance of the sensor for the detection of a broad range of glucose concentrations. (<b>a</b>) Schematic of the dialysis procedure. A dialysis bag containing a mixture of glucose oxidase and human serum was used. This dialysis bag was kept in the container of human serum to reduce the glucose concentration of the serum. The time vs. glucose concentration is the glucose attrition of the serum measured with a standard strip sensor; (<b>b</b>) Performance of the needle-type sensor. The sensor shows a peak at 1800 s for the batch 1 samples (dialyzed human serum: 45 mL/dL, 100 mL/dL, 250 mL/dL, and 500 mL/dL), which have glucose concentrations comparable to that observed in hypoglycemia, whereas this peak is not observed in the profile for batch 2 (normal human serum: 100 mL/dL, 250 mL/dL, and 500 mL/dL). Averaged standard deviation for a constant glucose concentration (i.e., 100 mg/dL and t = 1800–3600 s of batch 2) was measured as 0.005496 nm, corresponding to 2.77% of the wavelength shift; (<b>c</b>) Repeatability test results (<span class="html-italic">n</span> = 3) for glucose concentration range of 100–500 mg/dL.</p>
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<p>Comparison of the needle-type sensor with the standard bio-layer interference (BLI) sensor. Nearly identical profiles were observed when tested with a wide range of glucose concentrations. The profiles indicate that the reproducibility of the needle-type sensor is more stable than the standard BLI sensor.</p>
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2626 KiB  
Article
A Portable Real-Time Ringdown Breath Acetone Analyzer: Toward Potential Diabetic Screening and Management
by Chenyu Jiang, Meixiu Sun, Zhennan Wang, Zhuying Chen, Xiaomeng Zhao, Yuan Yuan, Yingxin Li and Chuji Wang
Sensors 2016, 16(8), 1199; https://doi.org/10.3390/s16081199 - 30 Jul 2016
Cited by 47 | Viewed by 7773
Abstract
Breath analysis has been considered a suitable tool to evaluate diseases of the respiratory system and those that involve metabolic changes, such as diabetes. Breath acetone has long been known as a biomarker for diabetes. However, the results from published data by far [...] Read more.
Breath analysis has been considered a suitable tool to evaluate diseases of the respiratory system and those that involve metabolic changes, such as diabetes. Breath acetone has long been known as a biomarker for diabetes. However, the results from published data by far have been inconclusive regarding whether breath acetone is a reliable index of diabetic screening. Large variations exist among the results of different studies because there has been no “best-practice method” for breath-acetone measurements as a result of technical problems of sampling and analysis. In this mini-review, we update the current status of our development of a laser-based breath acetone analyzer toward real-time, one-line diabetic screening and a point-of-care instrument for diabetic management. An integrated standalone breath acetone analyzer based on the cavity ringdown spectroscopy technique has been developed. The instrument was validated by using the certificated gas chromatography-mass spectrometry. The linear fittings suggest that the obtained acetone concentrations via both methods are consistent. Breath samples from each individual subject under various conditions in total, 1257 breath samples were taken from 22 Type 1 diabetic (T1D) patients, 312 Type 2 diabetic (T2D) patients, which is one of the largest numbers of T2D subjects ever used in a single study, and 52 non-diabetic healthy subjects. Simultaneous blood glucose (BG) levels were also tested using a standard diabetic management BG meter. The mean breath acetone concentrations were determined to be 4.9 ± 16 ppm (22 T1D), and 1.5 ± 1.3 ppm (312 T2D), which are about 4.5 and 1.4 times of the one in the 42 non-diabetic healthy subjects, 1.1 ± 0.5 ppm, respectively. A preliminary quantitative correlation (R = 0.56, p < 0.05) between the mean individual breath acetone concentration and the mean individual BG levels does exist in 20 T1D subjects with no ketoacidosis. No direct correlation is observed in T1D subjects, T2D subjects, and healthy subjects. The results from a relatively large number of subjects tested indicate that an elevated mean breath acetone concentration exists in diabetic patients in general. Although many physiological parameters affect breath acetone, under a specifically controlled condition fast (<1 min) and portable breath acetone measurement can be used for screening abnormal metabolic status including diabetes, for point-of-care monitoring status of ketone bodies which have the signature smell of breath acetone, and for breath acetone related clinical studies requiring a large number of tests. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>Schematic of the integrated standalone CRDS breath acetone analyzer (LaserBreath-001).</p>
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<p>Schematic diagram of the optical layout in the ringdown breath analyzer.</p>
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<p>The typical instrument stability in terms of the ringdown time obtained. Each data point is generated from averaging 100 ringdown events, a stability σ/<math display="inline"> <semantics> <mover accent="true"> <mi mathvariant="sans-serif">τ</mi> <mo>¯</mo> </mover> </semantics> </math> of 0.17% was measured.</p>
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<p>The instrument shows good reproducibility in terms of the ringdown times obtained at different pressures: 0 Torr, 730 Torr nitrogen, and the laboratory atmosphere.</p>
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<p>The instrument response to various samples, such as cylinder nitrogen, cylinder acetone helium mixture, laboratory air, hallway air, and breath samples.</p>
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<p>The instrument response to various samples and the effectiveness of humidity removal using a membrane filter.</p>
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<p>The ringdown breath analyzer’s performance validated by a certified GC-MS facility.</p>
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<p>Mean breath acetone concentrations in 22 T1D subjects, 312 T2D subjects, and 52 non-diabetic subjects, under four different conditions: fasting, 2 h post-breakfast, 2 h post-lunch, and 2 h post-dinner. The error bar corresponds to one standard deviation.</p>
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<p>The measured breath acetone concentrations versus BG levels, (<b>a</b>) 22 T1D subjects; (<b>b</b>) 312 T2D subjects; and (<b>c</b>) 52 healthy subjects.</p>
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<p>Observed correlation of the mean breath acetone concentration (ppm) with the mean blood glucose level in all T1D subjects with no ketoacidosis. The two samples with ketoacidosis are marked in dashed circles.</p>
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2370 KiB  
Article
Nitrogen-Doped Carbon Dots as A New Substrate for Sensitive Glucose Determination
by Hanxu Ji, Feng Zhou, Jiangjiang Gu, Chen Shu, Kai Xi and Xudong Jia
Sensors 2016, 16(5), 630; https://doi.org/10.3390/s16050630 - 4 May 2016
Cited by 54 | Viewed by 9333
Abstract
Nitrogen-doped carbon dots are introduced as a novel substrate suitable for enzyme immobilization in electrochemical detection metods. Nitrogen-doped carbon dots are easily synthesised from polyacrylamide in just one step. With the help of the amino group on chitosan, glucose oxidase is immobilized on [...] Read more.
Nitrogen-doped carbon dots are introduced as a novel substrate suitable for enzyme immobilization in electrochemical detection metods. Nitrogen-doped carbon dots are easily synthesised from polyacrylamide in just one step. With the help of the amino group on chitosan, glucose oxidase is immobilized on nitrogen-doped carbon dots-modified carbon glassy electrodes by amino-carboxyl reactions. The nitrogen-induced charge delocalization at nitrogen-doped carbon dots can enhance the electrocatalytic activity toward the reduction of O2. The specific amino-carboxyl reaction provides strong and stable immobilization of GOx on electrodes. The developed biosensor responds efficiently to the presence of glucose in serum samples over the concentration range from 1 to 12 mM with a detection limit of 0.25 mM. This novel biosensor has good reproducibility and stability, and is highly selective for glucose determination under physiological conditions. These results indicate that N-doped quantum dots represent a novel candidate material for the construction of electrochemical biosensors. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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Graphical abstract
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<p>Schematic of the N-doped CDs based electrochemical glucose biosensor.</p>
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<p>(<b>a</b>) UV-vis absorption spectrum of N-doped CDs; (<b>b</b>) The typical TEM image of the N-doped CDs; (<b>c</b>) PL spectrum of the N-doped CDs with excitation at 280 nm; (<b>d</b>) XPS full scan spectrum of the N-doped CDs, inset is XPS N 1s spectrum of the N-doped CDs.</p>
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<p>(<b>A</b>): Cyclic voltammograms of (<b>a</b>) non-N-doped CDs modified electrode and (<b>b</b>) N-doped CDs modified electrode in air-saturated 0.1 M pH 7.0 PBS. Scan rate: 100 m·Vs<sup>−1</sup> (<b>B</b>): Electrochemical response for two types of CDs (<b>a</b>: non-N-doped CDs and <b>b</b>: N-doped CDs); (<b>C</b>) the picture of modified electrode.</p>
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<p>(<b>a</b>) Cyclic voltammograms N-doped CDs modified electrode in air-saturated PBS containing 10% of serum sample at a scan rate of 100 mV·s<sup>−1</sup> with the addition of different concentrations of glucose; (<b>b</b>) Electrochemical response curve for different concentration of glucose.</p>
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<p>Determination of reproducibility of developed GOx/N-doped CDs/chitosan modified GCE in air-saturated PBS containing 10% of serum sample with: (<b>a</b>) 1 mM and (<b>b</b>) 5 mM glucose.</p>
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<p>Determination of reproducibility of developed GOx/N-doped CDs/chitosan modified GCE in air-saturated PBS with 1 mM a glucose and in air-saturated PBS containing 10% of serum samples.</p>
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Review

Jump to: Research

2605 KiB  
Review
Current and Emerging Technology for Continuous Glucose Monitoring
by Cheng Chen, Xue-Ling Zhao, Zhan-Hong Li, Zhi-Gang Zhu, Shao-Hong Qian and Andrew J. Flewitt
Sensors 2017, 17(1), 182; https://doi.org/10.3390/s17010182 - 19 Jan 2017
Cited by 228 | Viewed by 52455
Abstract
Diabetes has become a leading cause of death worldwide. Although there is no cure for diabetes, blood glucose monitoring combined with appropriate medication can enhance treatment efficiency, alleviate the symptoms, as well as diminish the complications. For point-of-care purposes, continuous glucose monitoring (CGM) [...] Read more.
Diabetes has become a leading cause of death worldwide. Although there is no cure for diabetes, blood glucose monitoring combined with appropriate medication can enhance treatment efficiency, alleviate the symptoms, as well as diminish the complications. For point-of-care purposes, continuous glucose monitoring (CGM) devices are considered to be the best candidates for diabetes therapy. This review focuses on current growth areas of CGM technologies, specifically focusing on subcutaneous implantable electrochemical glucose sensors. The superiority of CGM systems is introduced firstly, and then the strategies for fabrication of minimally-invasive and non-invasive CGM biosensors are discussed, respectively. Finally, we briefly outline the current status and future perspective for CGM systems. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>The schematic illustration of G1.0 PAMAM-functionalized microgels that can recognize glucose and emit blue fluorescence after injection. Reprinted with permission from [<a href="#B36-sensors-17-00182" class="html-bibr">36</a>].</p>
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<p>Illustration of the electron transfer steps after illumination of the QD electrode. Reprinted with permission from [<a href="#B40-sensors-17-00182" class="html-bibr">40</a>].</p>
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<p>Schematic of PDMS chip utilization for monitoring of glucose solutions by a CMOS image sensor. Reprinted with permission from [<a href="#B44-sensors-17-00182" class="html-bibr">44</a>].</p>
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<p>Illustration of closed-loop glycemic management system utilizing the ‘Sense and Act’ method for optimized insulin delivery.</p>
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<p>Measurement method of tear glucose concentration with a contact lens biosensor. BG levels were simultaneously measured by a commercial BG monitoring kit. Reprinted with permission from [<a href="#B69-sensors-17-00182" class="html-bibr">69</a>].</p>
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<p>(<b>a</b>) Diagram and photograph (insert) of a physical hydrogel photonic crystal sensing lens; (<b>b</b>) Diffraction wavelength shifts with the variation of the glucose concentration in artificial tear solution.</p>
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<p>(<b>A</b>) Schematic image of the glucose biosensor on the polyethylene terephthalate glycol mouthguard support. Pt and Ag electrodes were formed on the PETG through a sputtering process. Each electrode sensor consisted of a 0.20 mm<sup>2</sup> Pt working electrode and a 4.0 mm<sup>2</sup> Ag/AgCl reference/counter electrode, both insulated with PDMS on a 0.5 mm thick PETG layer. 30 units of GOD were applied to the sensing region of the working electrode. In order to optimize enzyme entrapment, 2.0 mL of 1.0 wt% PMEH solution was spread over the sensing region to form the PMEH overcoat; (<b>B</b>) Schematic image of the mouth-guard biosensor custom-fit to the patient’s dentition. The device consists of a glucose sensor and wireless transmitter incorporating a potentiostat for stable glucose measurement. The sensor was designed to fit the mandibular dentition from the first premolar up to the third molar. The wireless transmitter was neatly encased in PETG. Reprinted with permission from [<a href="#B94-sensors-17-00182" class="html-bibr">94</a>].</p>
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411 KiB  
Review
The Clinical Benefits and Accuracy of Continuous Glucose Monitoring Systems in Critically Ill Patients—A Systematic Scoping Review
by Sigrid C. J. Van Steen, Saskia Rijkenberg, Jacqueline Limpens, Peter H. J. Van der Voort, Jeroen Hermanides and J. Hans DeVries
Sensors 2017, 17(1), 146; https://doi.org/10.3390/s17010146 - 14 Jan 2017
Cited by 45 | Viewed by 8771
Abstract
Continuous Glucose Monitoring (CGM) systems could improve glycemic control in critically ill patients. We aimed to identify the evidence on the clinical benefits and accuracy of CGM systems in these patients. For this, we performed a systematic search in Ovid MEDLINE, from inception [...] Read more.
Continuous Glucose Monitoring (CGM) systems could improve glycemic control in critically ill patients. We aimed to identify the evidence on the clinical benefits and accuracy of CGM systems in these patients. For this, we performed a systematic search in Ovid MEDLINE, from inception to 26 July 2016. Outcomes were efficacy, accuracy, safety, workload and costs. Our search retrieved 356 articles, of which 37 were included. Randomized controlled trials on efficacy were scarce (n = 5) and show methodological limitations. CGM with automated insulin infusion improved time in target and mean glucose in one trial and two trials showed a decrease in hypoglycemic episodes and time in hypoglycemia. Thirty-two articles assessed accuracy, which was overall moderate to good, the latter mainly with intravascular devices. Accuracy in critically ill children seemed lower than in adults. Adverse events were rare. One study investigated the effect on workload and cost, and showed a significant reduction in both. In conclusion, studies on the efficacy and accuracy were heterogeneous and difficult to compare. There was no consistent clinical benefit in the small number of studies available. Overall accuracy was moderate to good with some intravascular devices. CGM systems seemed however safe, and might positively affect workload and costs. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>Flow diagram of study selection.</p>
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4868 KiB  
Review
A Review on Microfluidic Paper-Based Analytical Devices for Glucose Detection
by Shuopeng Liu, Wenqiong Su and Xianting Ding
Sensors 2016, 16(12), 2086; https://doi.org/10.3390/s16122086 - 8 Dec 2016
Cited by 97 | Viewed by 13895
Abstract
Glucose, as an essential substance directly involved in metabolic processes, is closely related to the occurrence of various diseases such as glucose metabolism disorders and islet cell carcinoma. Therefore, it is crucial to develop sensitive, accurate, rapid, and cost effective methods for frequent [...] Read more.
Glucose, as an essential substance directly involved in metabolic processes, is closely related to the occurrence of various diseases such as glucose metabolism disorders and islet cell carcinoma. Therefore, it is crucial to develop sensitive, accurate, rapid, and cost effective methods for frequent and convenient detections of glucose. Microfluidic Paper-based Analytical Devices (μPADs) not only satisfying the above requirements but also occupying the advantages of portability and minimal sample consumption, have exhibited great potential in the field of glucose detection. This article reviews and summarizes the most recent improvements in glucose detection in two aspects of colorimetric and electrochemical μPADs. The progressive techniques for fabricating channels on μPADs are also emphasized in this article. With the growth of diabetes and other glucose indication diseases in the underdeveloped and developing countries, low-cost and reliably commercial μPADs for glucose detection will be in unprecedentedly demand. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>Scheme of the typical stamping process for microfluidic paper-based analytical devices (μPADs) fabrication: (<b>a</b>) a native paper (n-paper) is covered by a paraffinized paper (p-paper); (<b>b</b>) after heated at 150 °C, the metal stamp is pressed against the layered paper pieces; (<b>c</b>) a typical μPAD fabricated by the stamping process and its optical micrograph (<b>d</b>). With the permission from [<a href="#B62-sensors-16-02086" class="html-bibr">62</a>]; Copyright 2014, The Royal Society of Chemistry.</p>
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<p>Scheme of the μPAD fabrication in [<a href="#B59-sensors-16-02086" class="html-bibr">59</a>]: A filter paper mask (<b>b</b>) was obtained by cutting on a native filter paper (<b>a</b>), and was immersed in TMOS solution (<b>c</b>); The TMOS-adsorbed mask and a native filter paper were packed between two glass slides (<b>d</b>); TMOS molecules were assembled on the native filter paper by heating (<b>e</b>); and the fabricated μPAD with hydrophilic-hydrophobic contrast (<b>f</b>) and its photograph (<b>g</b>) obtained by spraying water on it. With the permission from [<a href="#B59-sensors-16-02086" class="html-bibr">59</a>]; Copyright 2014, The Royal Society of Chemistry.</p>
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<p>Scheme of the μPAD fabrication in [<a href="#B59-sensors-16-02086" class="html-bibr">59</a>]: A filter paper mask (<b>b</b>) was obtained by cutting on a native filter paper (<b>a</b>), and was immersed in TMOS solution (<b>c</b>); The TMOS-adsorbed mask and a native filter paper were packed between two glass slides (<b>d</b>); TMOS molecules were assembled on the native filter paper by heating (<b>e</b>); and the fabricated μPAD with hydrophilic-hydrophobic contrast (<b>f</b>) and its photograph (<b>g</b>) obtained by spraying water on it. With the permission from [<a href="#B59-sensors-16-02086" class="html-bibr">59</a>]; Copyright 2014, The Royal Society of Chemistry.</p>
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<p>Fabrication scheme of the μPAD designed in [<a href="#B91-sensors-16-02086" class="html-bibr">91</a>]. The central plasma separation zone (<b>a</b>) and the four test readout zones (<b>b</b>) were patterned on chromatography paper by a wax printer (<b>c</b>); (<b>d</b>) Agglutinating antibodies were immobilized at the central part while the reagents for the colorimetric assay at the periphery zones; (<b>e</b>) To perform a diagnostic test with the developed μPAD, the whole blood sample was dropped onto the plasma separation zone; (<b>f</b>) The red blood cells were agglutinated in the central zone, while the separated plasma wicked into the test readout zones and reacted with the reagents of the colorimetric assay. With the permission from [<a href="#B91-sensors-16-02086" class="html-bibr">91</a>]; Copyright 2012, The Royal Society of Chemistry.</p>
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<p>Scheme of the μPAD fabrication in [<a href="#B35-sensors-16-02086" class="html-bibr">35</a>] (<b>a</b>) Poly(DAB) as a brown precipitate was generated in the present of peroxidase when DAB reacted with H<sub>2</sub>O<sub>2</sub> which came from the oxidation of glucose under the existing of glucose oxidase. By the capillary effect, the brown distance along the channels was related to the concentration of glucose involved in the reactions; (<b>b</b>) The standard calibration curves (closed blue squares) of the color development distance with the standard glucose solutions. The real (complex) serum samples (opened squares) contained 100 nM glucose according to the color development distance. With the permission from [<a href="#B35-sensors-16-02086" class="html-bibr">35</a>]; Copyright 2013, The Royal Society of Chemistry.</p>
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<p>(<b>a</b>) Scanning electron microscope (SEM) images of the μPAD in [<a href="#B100-sensors-16-02086" class="html-bibr">100</a>] after the SiO<sub>2</sub> nanoparticles deposition. Optical images of the μPADs with (<b>c</b>) and without (<b>b</b>) SiO<sub>2</sub> nanoparticles modification applied to the colorimetric assay for glucose. Adapted with the permission from [<a href="#B100-sensors-16-02086" class="html-bibr">100</a>]; Copyright 2014, The Royal Society of Chemistry.</p>
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<p>The paper-based 3D-μPADs designed in [<a href="#B115-sensors-16-02086" class="html-bibr">115</a>]. (<b>a</b>) The yellow rectangles of the unfolded μPADs marked out the protein detection region while the orange rectangles for glucose; (<b>b</b>) Flow pattern of the developed 3D-μPADs. Sample flow was introduced from the corner at the top left and spread into the middle square at the bottom layer. With the permission from [<a href="#B115-sensors-16-02086" class="html-bibr">115</a>]; Copyright 2013, American Chemical Society.</p>
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<p>Scheme of the 3D-μPAD formation on a single sheet of paper in [<a href="#B116-sensors-16-02086" class="html-bibr">116</a>]. Before (<b>a</b>) and after (<b>b</b>) loading the red dye solution, the front, backside and cross section images of each parts indicated that the red dye solution had smoothly flowed from the inlet to the outlet via the alternative lower and upper channels. With the permission from [<a href="#B116-sensors-16-02086" class="html-bibr">116</a>]; Copyright 2015, The Royal Society of Chemistry.</p>
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<p>Photographs of electrochemical PADs built up on: A4 papers (<b>a</b>,<b>b</b>); and paper cups (<b>c</b>). Adapted with the permission from [<a href="#B123-sensors-16-02086" class="html-bibr">123</a>]; Copyright 2015, The Royal Society of Chemistry.</p>
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4806 KiB  
Review
Conducting Polymers and Their Applications in Diabetes Management
by Yu Zhao, Luyao Cao, Lanlan Li, Wen Cheng, Liangliang Xu, Xinyu Ping, Lijia Pan and Yi Shi
Sensors 2016, 16(11), 1787; https://doi.org/10.3390/s16111787 - 26 Oct 2016
Cited by 28 | Viewed by 8694
Abstract
Advances in conducting polymers (CPs) have promoted the development of diabetic monitoring and treatment, which is of great significance in human healthcare and modern medicine. CPs are special polymers with physical and electrochemical features resembling metals, inorganic semiconductors and non-conducting polymers. To improve [...] Read more.
Advances in conducting polymers (CPs) have promoted the development of diabetic monitoring and treatment, which is of great significance in human healthcare and modern medicine. CPs are special polymers with physical and electrochemical features resembling metals, inorganic semiconductors and non-conducting polymers. To improve and extend their properties, the fabrication of CPs and CP composites has attracted intensive attention in recent decades. Some CPs are biocompatible and suitable for biomedical use. Thus, the intriguing properties of CPs make wearable, noninvasive, continuous diabetes managing devices and other potential applications in diabetes possible in the near future. To highlight the recent advances of CPs and their derived materials (especially in conducting polymer hydrogels), here we discuss their fabrication and characterization, review the current state-of-the-art research in diabetes management based on these materials and describe current challenges as well as future potential research directions. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>Molecular structures of typical conductive polymers.</p>
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<p>Doping of conducting polymer (polypyrrole, PPy) with ionic species A.</p>
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<p>Schematic of a typical example of an electrochemical biosensor based on CPHs and a list of the main properties of CPHs. Reprinted with permission from [<a href="#B16-sensors-16-01787" class="html-bibr">16</a>]. Copyright (2015) The Royal Society of Chemistry.</p>
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<p>(<b>a</b>,<b>b</b>) SEM image of the AAO membrane before (a) and after (b) formation of PANI nanotubes in the pores of the template. (<b>c</b>,<b>d</b>) SEM images of polyaniline nanotubes obtained by etching away the AAO membrane. (<b>e</b>) Cross-sectional image of the polyaniline nanotube after loading GOx on the inner wall of the nanotube. Reprinted with permission from [<a href="#B43-sensors-16-01787" class="html-bibr">43</a>]. Copyright (2009) American Chemical Society.</p>
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<p>(<b>a</b>) A conducting cross-linked PANI-based redox hydrogel. (<b>b</b>) Dependence of the current density on the glucose oxidase weight percentage, with glucose concentration maintained at 32 mM. (<b>c</b>) Dependence of the steady-state current density on the glucose concentration for an electrode set at +0.3 V vs. Ag/AgCl. Reprinted with permission from [<a href="#B60-sensors-16-01787" class="html-bibr">60</a>]. Copyright (2007) American Chemical Society.</p>
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<p>(<b>a</b>) Amperometric response of the PtNP/PANI hydrogel electrode after successive addition of glucose in 0.1 M PBS (pH = 5.6) at an applied potential of 0.56 V (the magnified part of the curve is marked with a red square); (<b>b</b>) the calibration curve for glucose concentrations from 1 μM to 80 mM. Reprinted with permission from [<a href="#B66-sensors-16-01787" class="html-bibr">66</a>]. Copyright (2013) American Chemical Society.</p>
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<p>(<b>a</b>) The integrated wearable diabetes controlling and detecting system connected to a portable electrochemical analyser; (<b>b</b>) GP-hybrid electrochemical device array on the human skin with perspiration; (<b>c</b>) RH measurement by the diabetes patch. (<b>d</b>) Measurement of the pH variation in two human sweat samples from two subjects; (<b>e</b>) One-day monitoring of glucose concentrations in the sweat and blood of a human (subject 2 in d); (<b>f</b>) Comparison of the average glucose concentrations with the commercial glucose assay data in e before and after correction using the measured pH (error bars show the standard deviation); (<b>g</b>) Plots showing the stable sensitivity of the glucose and pH sensors after multiple reuses of the patch; (<b>h</b>) Schematic illustrations of bioresorbable microneedles. (<b>i</b>) Drug release from the microneedles at different temperatures (N = 3, error bars show the standard deviation); (<b>j</b>) Infrared camera images of multichannel heaters showing the stepwise drug release; (<b>k</b>) Optical images of the stepwise dissolution of the microneedles; (<b>l</b>) Image of the heater integrated with the microneedles, which is laminated on the skin near the abdomen of the db/db mouse. The hair on the skin was shaved off before treatment with the microneedles; (<b>m</b>) Optical image (left) and its magnified view (right) of the db/db mouse skin stained with trypan blue to visualize the micro-sized holes made by the penetration of the microneedles; (<b>n</b>) Optical (left) and infrared (right) camera images of the patch with the thermal actuation; (<b>o</b>) Blood glucose concentrations of db/db mice for the treated group (with the drug) and control groups (without the patch and without the drug). The error bars show the standard deviation in each group and small P values show that the results are statistically reliable. The asterisks indicate significant difference (P &lt; 0.05) between the treated (red) and the non-treated group (blue and green) on each time point. Reprinted with permission from [<a href="#B70-sensors-16-01787" class="html-bibr">70</a>]. Copyright (2016) Macmillan Publishers Limited. All rights reserved.</p>
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<p>(<b>a</b>) The integrated wearable diabetes controlling and detecting system connected to a portable electrochemical analyser; (<b>b</b>) GP-hybrid electrochemical device array on the human skin with perspiration; (<b>c</b>) RH measurement by the diabetes patch. (<b>d</b>) Measurement of the pH variation in two human sweat samples from two subjects; (<b>e</b>) One-day monitoring of glucose concentrations in the sweat and blood of a human (subject 2 in d); (<b>f</b>) Comparison of the average glucose concentrations with the commercial glucose assay data in e before and after correction using the measured pH (error bars show the standard deviation); (<b>g</b>) Plots showing the stable sensitivity of the glucose and pH sensors after multiple reuses of the patch; (<b>h</b>) Schematic illustrations of bioresorbable microneedles. (<b>i</b>) Drug release from the microneedles at different temperatures (N = 3, error bars show the standard deviation); (<b>j</b>) Infrared camera images of multichannel heaters showing the stepwise drug release; (<b>k</b>) Optical images of the stepwise dissolution of the microneedles; (<b>l</b>) Image of the heater integrated with the microneedles, which is laminated on the skin near the abdomen of the db/db mouse. The hair on the skin was shaved off before treatment with the microneedles; (<b>m</b>) Optical image (left) and its magnified view (right) of the db/db mouse skin stained with trypan blue to visualize the micro-sized holes made by the penetration of the microneedles; (<b>n</b>) Optical (left) and infrared (right) camera images of the patch with the thermal actuation; (<b>o</b>) Blood glucose concentrations of db/db mice for the treated group (with the drug) and control groups (without the patch and without the drug). The error bars show the standard deviation in each group and small P values show that the results are statistically reliable. The asterisks indicate significant difference (P &lt; 0.05) between the treated (red) and the non-treated group (blue and green) on each time point. Reprinted with permission from [<a href="#B70-sensors-16-01787" class="html-bibr">70</a>]. Copyright (2016) Macmillan Publishers Limited. All rights reserved.</p>
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<p>Schematic of the glucose-responsive insulin delivery system using MN-array patches. (<b>a</b>) Formation of GRVs composed of HS-HA; (<b>b</b>) Schematic of the GRV-containing MN-array insulin patch for in vivo insulin delivery triggered by a hyperglycemic state to release more insulin. Reprinted with permission from [<a href="#B71-sensors-16-01787" class="html-bibr">71</a>]. Copyright (2015) National Academy of Sciences.</p>
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Review
Responsive Boronic Acid-Decorated (Co)polymers: From Glucose Sensors to Autonomous Drug Delivery
by Gertjan Vancoillie and Richard Hoogenboom
Sensors 2016, 16(10), 1736; https://doi.org/10.3390/s16101736 - 19 Oct 2016
Cited by 31 | Viewed by 9763
Abstract
Boronic acid-containing (co)polymers have fascinated researchers for decades, garnering attention for their unique responsiveness toward 1,2- and 1,3-diols, including saccharides and nucleotides. The applications of materials that exert this property are manifold including sensing, but also self-regulated drug delivery systems through responsive membranes [...] Read more.
Boronic acid-containing (co)polymers have fascinated researchers for decades, garnering attention for their unique responsiveness toward 1,2- and 1,3-diols, including saccharides and nucleotides. The applications of materials that exert this property are manifold including sensing, but also self-regulated drug delivery systems through responsive membranes or micelles. In this review, some of the main applications of boronic acid containing (co)polymers are discussed focusing on the role of the boronic acid group in the response mechanism. We hope that this summary, which highlights the importance and potential of boronic acid-decorated polymeric materials, will inspire further research within this interesting field of responsive polymers and polymeric materials. Full article
(This article belongs to the Special Issue Glucose Sensors: Revolution in Diabetes Management 2016)
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<p>Simplified boronic acid equilibrium in the presence of a diol.</p>
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<p>(<b>Left</b>) Images of swelling behavior of a P(NIPAAm-<span class="html-italic">co</span>-mAPBA) macrogel over time in the presence of 5 g/L <span class="html-small-caps">d</span>-Glu solution in pH 9 CHES buffer (T = 25 °C) [<a href="#B12-sensors-16-01736" class="html-bibr">12</a>]. Copyright 2004 American Chemical Society; (<b>Right</b>) Relative turbidity of P(NIPAAm<span class="html-italic">-co-m</span>APBA) microgel dispersion changes in function of time upon addition of <span class="html-small-caps">d</span>-Glu in 0.020 M pH 8.5 phosphate buffer (T = 25 °C) [<a href="#B13-sensors-16-01736" class="html-bibr">13</a>]. Copyright 2011 American Chemical Society.</p>
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<p>(<b>A</b>) Monomer structures and schematic representation of the polymeric framboidal aggregate with TEM (top) and AFM (bottom) picture; (<b>B</b>) Swelling behavior of the PBA-NPs with the reversible swelling behavior upon pH change between pH 5 and pH 10 as measured by DLS (left) and the diameter change of PBA-NPs upon the addition of <span class="html-small-caps">d</span>-glucose (triangle) and <span class="html-small-caps">d</span>-fructose (circle) as measured by DLS [<a href="#B16-sensors-16-01736" class="html-bibr">16</a>]. Copyright 2015 American Chemical Society.</p>
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<p>Structure and schematic representation of the micellar association/dissociation under the influence of glucose at physiological pH for P(DMA-block-DDOPBA) [<a href="#B21-sensors-16-01736" class="html-bibr">21</a>]. Copyright 2012 American Chemical Society.</p>
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<p>Schematic representation of glucose-triggered disassembly of polymersomes of sugar-responsive block copolymers [<a href="#B47-sensors-16-01736" class="html-bibr">47</a>]. Copyright 2012 American Chemical Society.</p>
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<p>Schematic representation of the fabricated polymer vesicles using a αCD/PEG inclusion complex-template strategy [<a href="#B49-sensors-16-01736" class="html-bibr">49</a>]. Copyright 2015 American Chemical Society.</p>
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<p>Schematic representation of a protein-loaded core-cross-linked micelle through boronate ester-catechol complex formation and subsequent intracellular protein delivery triggered by endosomal pH [<a href="#B61-sensors-16-01736" class="html-bibr">61</a>]. Copyright 2013 American Chemical Society.</p>
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<p>Schematic representation of a polymer phase transition in aqueous solution and a description of the significant changes in the local environment of the polymer. Adapted from [<a href="#B81-sensors-16-01736" class="html-bibr">81</a>].</p>
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<p>(<b>A</b>) Synthetic schemes employed for the preparation of thermo- and glucose-responsive P(NIPAM-APBA-NBDAE-RhBEA) fluorescent microgels via emulsion polymerization; (<b>B</b>) Schematic illustration for the modulation of FRET efficiencies within P(NIPAM-APBA-NBDAE-RhBEA) microgels by temperature variations and the addition of glucose [<a href="#B88-sensors-16-01736" class="html-bibr">88</a>]. Copyright 2011 American Chemical Society.</p>
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<p>(<b>Left</b>) Schematic representation of electrostatically induced excimer formation of positively charged trimethylpentylammonium pyrene salt; (<b>Right</b>) Emission spectra (normalized at 375 nm) with increased glucose concentration showing excimer emission enhancement between 450 nm and 650 nm [<a href="#B93-sensors-16-01736" class="html-bibr">93</a>]. Reproduced by permission of The Royal Society of Chemistry.</p>
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<p>(<b>Left</b>) PCCA photonic crystal sensing materials consist of an embedded CCA surrounded by a polymer hydrogel network that contains boronic acid moieties (red circles). The imbedded PS-particles (green circles) diffract light of a wavelength determined by the array lattice constant; (<b>Right</b>) The change in diffracted wavelength resulting from the hydrogel volume swelling upon interaction with glucose as analyte [<a href="#B94-sensors-16-01736" class="html-bibr">94</a>]. Copyright 2003 American Chemical Society.</p>
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