Applied System on Biomedical Engineering, Healthcare and Sustainability 2024

Topic Information

Dear Colleagues,

Recently, the healthcare sector is undergoing a transformation due to advances in computing, networking technologies, big data, and artificial intelligence. Healthcare is not only changing from reactive and hospital centered to preventive and personalized, but is also changing from disease focused to focusing on well-being. Healthcare systems, as well as fundamental medicinal research, are becoming smarter and more enabled in Biomedical Engineering. Furthermore, with cutting-edge sensors and computer technologies, healthcare delivery could also yield better efficiency, higher quality, and lower costs. However, these innovations often do not result in sustainability, health, and happiness for all people. Science and technology need to be complemented by arts, humanities, social sciences, as well indigenous knowledge and wisdom if we are to increase the accessibility of the benefits for those in need across all regions and classes of people. We need ethically aligned and driven health care systems and sustainability. This topic “Applied System on Biomedical Engineering, Healthcare, and Sustainability 2024” includes the five following journals: Applied Sciences, ASI, Bioengineering, Electronics, and Healthcare. This enables the interdisciplinary collaboration of science and engineering technologists in the academic and industrial fields, as well as international networking.

Topics of interest include as followings:

• Smart healthcare system analysis and design
• Computer and human–machine interactions of healthcare system
• Application of IoT (Internet of Things) on healthcare system
• Big data and artificial intelligence enabled healthcare systems
• Health-related aspects of sustainability
• Environmental education and public health
• Environmental engineering and biotechnology Rehabilitation Medicine and Physiotherapy
• Sports Medicine
• Pediatric and Geriatric Emergency Care
• Leisure recreation
• Health promotion
• Nourishment and health care
• Disaster and Health
• Health and Environment
• Health Services
• Occupational Health
• Impact of safety, security, and disaster management on sustainability
• Sustainability science 
• Medical electronics
• Biomedical materials
• Biomedical diagnostic techniques
• Medical information and rehabilitation technology
• Other related topics in Healthcare, Sustainability, Biomedical Engineering.

Prof. Dr. Teen-­Hang Meen
Prof. Dr. Chun-Yen Chang
Prof. Dr. Charles Tijus
Prof. Dr. Po-Lei Lee
Prof. Dr. Kuei-Shu Hsu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Applied System Innovation asi	3.8	7.9	2018	31.4 Days	CHF 1400	Submit
Bioengineering bioengineering	3.8	4.0	2014	16.4 Days	CHF 2700	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Healthcare healthcare	2.4	3.5	2013	20.3 Days	CHF 2700	Submit

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (16 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Applied Sciences ASI Bioengineering Electronics Healthcare

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

34 pages, 42799 KiB  

Open AccessArticle

YOLO-DentSeg: A Lightweight Real-Time Model for Accurate Detection and Segmentation of Oral Diseases in Panoramic Radiographs

by Yue Hua, Rui Chen and Hang Qin

Electronics 2025, 14(4), 805; https://doi.org/10.3390/electronics14040805 - 19 Feb 2025

Abstract

Panoramic radiography is vital in dentistry, where accurate detection and segmentation of diseased regions aid clinicians in fast, precise diagnosis. However, the current methods struggle with accuracy, speed, feature extraction, and suitability for low-resource devices. To overcome these challenges, this research introduces a [...] Read more.

Panoramic radiography is vital in dentistry, where accurate detection and segmentation of diseased regions aid clinicians in fast, precise diagnosis. However, the current methods struggle with accuracy, speed, feature extraction, and suitability for low-resource devices. To overcome these challenges, this research introduces a unique YOLO-DentSeg model, a lightweight architecture designed for real-time detection and segmentation of oral dental diseases, which is based on an enhanced version of the YOLOv8n-seg framework. First, the C2f(Channel to Feature Map)-Faster structure is introduced in the backbone network, achieving a lightweight design while improving the model accuracy. Next, the BiFPN(Bidirectional Feature Pyramid Network) structure is employed to enhance its multi-scale feature extraction capabilities. Then, the EMCA(Enhanced Efficient Multi-Channel Attention) attention mechanism is introduced to improve the model’s focus on key disease features. Finally, the Powerful-IOU(Intersection over Union) loss function is used to optimize the detection box localization accuracy. Experiments show that YOLO-DentSeg achieves a detection precision (mAP50(Box)) of 87%, segmentation precision (mAP50(Seg)) of 85.5%, and a speed of 90.3 FPS. Compared to YOLOv8n-seg, it achieves superior precise and faster inference times while decreasing the model size, computational load, and parameter count by 44.9%, 17.5%, and 44.5%, respectively. YOLO-DentSeg enables fast, accurate disease detection and segmentation, making it practical for devices with limited computing power and ideal for real-world dental applications. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>Schematic diagram of oral disease detection and segmentation.</p> Full article ">Figure 2
<p>YOLO-DentSeg model structure diagram.</p> Full article ">Figure 3
<p>PConv schematic.</p> Full article ">Figure 4
<p>Comparison of Faster-Block and Bottleneck structures.</p> Full article ">Figure 5
<p>C2f-Faster schematic.</p> Full article ">Figure 6
<p>FPN, PANet, and BiFPN structures.</p> Full article ">Figure 7
<p>EMCA schematic of attention mechanisms.</p> Full article ">Figure 8
<p>Schematics of CIOU and PowerIOU. (<b>a</b>) The structure of the original YOLOv8 boundary box loss function, CIoU (Complete Intersection over Union); (<b>b</b>) The structure of the proposed boundary box loss function, Powerful-IoU.</p> Full article ">Figure 9
<p>The images before and after data augmentation.</p> Full article ">Figure 10
<p>Comparison of detection and segmentation accuracy averages prior to and following model enhancement.</p> Full article ">Figure 11
<p>Experimental curves for ablation experiments.</p> Full article ">Figure 12
<p>Adding experimental curves for different attention modules.</p> Full article ">Figure 13
<p>Experimental curves with various employed loss functions.</p> Full article ">Figure 14
<p>Scatterplots of different model experiments. (<b>A</b>) The relationship between the number of parameters and FPS (Frames Per Second) for each model; (<b>B</b>) The relationship between computational complexity (FLOPs) and FPS for each model; (<b>C</b>) The relationship between FPS and mAP50 (Box) for each model; (<b>D</b>) The relationship between FPS and mAP50 (Seg) for each model.</p> Full article ">Figure 15
<p>Detection segmentation results for different models.</p> Full article ">

18 pages, 5510 KiB  

Open AccessArticle

A New Design for Switched-Mode Dental Iontophoresis System Using a Dual-Return Probe

by Serkan Dişlitaş

Appl. Sci. 2025, 15(4), 1748; https://doi.org/10.3390/app15041748 - 8 Feb 2025

Abstract

In practice, continuous and pulse direct current (DC) methods are embodied in classical dental iontophoresis systems (CDISs) for the treatment of dentin hypersensitivity (DH). Changes in body electrical resistance and polarization occurrence are the main problems in dental iontophoresis applications. Moreover, continuous DC [...] Read more.

In practice, continuous and pulse direct current (DC) methods are embodied in classical dental iontophoresis systems (CDISs) for the treatment of dentin hypersensitivity (DH). Changes in body electrical resistance and polarization occurrence are the main problems in dental iontophoresis applications. Moreover, continuous DC application may cause discomforts such as irritation, burning and itching on the skin. For these reasons, it is preferred to use pulse DC instead of continuous DC. However, in pulse DC applications, the treatment period is prolonged depending on the decrease in the electrical charge flow. On the other hand, the pain threshold of teeth when the electric current is applied varies from person to person. In this study, in order to reduce the problems caused by the use of CDIS methods for the treatment of DH, a microcontroller-based switched-mode dental iontophoresis system (SMDIS) using a dual-return probe (RP) is designed, and its performance is compared with CDIS methods. According to the results, the new SMDIS both reduces the polarization effect as in the classical pulse DC method and shortens the prolonged treatment duration in pulse DC by raising the pain threshold of teeth due to increased ion transfer, which is a great advantage over former methods. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>Exposure of dentine tubules which causes DH due to enamel loss and gingival recession.</p> Full article ">Figure 2
<p>Electrical equivalent circuit model of the human skin.</p> Full article ">Figure 3
<p>Principle scheme of classical dental iontophoresis method.</p> Full article ">Figure 4
<p>Developed switched-mode pulse DC method.</p> Full article ">Figure 5
<p>Simplified electrical equivalent circuit model between hand and tooth for switched-mode pulse DC method.</p> Full article ">Figure 6
<p>CDIS/SMDIS test system: (<b>a</b>) with TP and RPs; (<b>b</b>) with human body electrical impedance prototype.</p> Full article ">Figure 7
<p>The general block diagram of the developed SMDIS and prototype.</p> Full article ">Figure 8
<p>An example screenshot of the developed GUI for dental iontophoresis experimental setup.</p> Full article ">Figure 9
<p>Flowchart of microcontroller software.</p> Full article ">Figure 10
<p>Experimental setup.</p> Full article ">Figure 11
<p>Average iontophoresis current changes depending on the applied DC voltages (0–5 V, 1 kHz, 50%) using the CDIS and SMDIS methods.</p> Full article ">Figure 12
<p>Peak iontophoresis current changes depending on the applied DC voltages (0–5 V, 1 kHz, 50%) using the CDIS and SMDIS methods.</p> Full article ">Figure 13
<p>Average iontophoresis current changes depending on the applied DC voltage (3 V, 1 kHz) pulse values with different duty cycle rates using the SMDIS and CDIS.</p> Full article ">Figure 14
<p>Average iontophoresis current changes depending on the applied DC voltage (3 V) with different frequencies using the SMDIS and CDIS.</p> Full article ">Figure 15
<p>Comparison of iontophoresis current waveforms depending on the applied DC voltage (3 V, 1 kHz, 50%) using the SMDIS and CDIS.</p> Full article ">Figure 16
<p>Comparison of treatment durations depending on the desired treatment dose for peak current (250 μA, 1 kHz, 50%) using the SMDIS and CDIS.</p> Full article ">

18 pages, 4747 KiB  

Open AccessArticle

Evaluation of Permeability, Safety, and Stability of Nanosized Ketoprofen Co-Spray-Dried with Mannitol for Carrier-Free Pulmonary Systems

by Heba Banat, Ilona Gróf, Mária A. Deli, Rita Ambrus and Ildikó Csóka

Appl. Sci. 2025, 15(3), 1547; https://doi.org/10.3390/app15031547 - 3 Feb 2025

Abstract

Pulmonary drug delivery presents a promising approach for managing respiratory diseases, enabling localized drug deposition and minimizing systemic side effects. Building upon previous research, this study investigates the cytotoxicity, permeability, and stability of a novel carrier-free dry powder inhaler (DPI) formulation comprising nanosized [...] Read more.

Pulmonary drug delivery presents a promising approach for managing respiratory diseases, enabling localized drug deposition and minimizing systemic side effects. Building upon previous research, this study investigates the cytotoxicity, permeability, and stability of a novel carrier-free dry powder inhaler (DPI) formulation comprising nanosized ketoprofen (KTP) and mannitol (MNT). The formulation was prepared using wet media milling to produce KTP-nanosuspensions, followed by spray drying to achieve combined powders suitable for inhalation. Cell viability and permeability were conducted in both alveolar (A549) and bronchial (CFBE) models. Stability was assessed after storage in hydroxypropyl methylcellulose (HPMC) capsules under stress conditions (40 °C, 75% RH), as per ICH guidelines. KTP showed good penetration through both models, with lower permeability through the CFBE barrier. The MNT-containing sample (F1) increased permeability by 1.4-fold in A549. All formulations had no effect on cell barrier integrity or viability after the impedance test, confirming their safety. During stability assessment, the particle size remained consistent, and the partially amorphous state of KTP was retained over time. However, moisture absorption induced surface roughening and partial agglomeration, leading to reduced fine particle fraction (FPF) and emitted fraction (EF). Despite these changes, the mass median aerodynamic diameter (MMAD) remained stable, confirming the formulation’s continued applicability for pulmonary delivery. Future research should focus on optimizing excipient content, alternative capsule materials, and storage conditions to mitigate moisture-related issues. Hence, the findings demonstrate that the developed ketoprofen–mannitol DPI retains its quality and performance characteristics over an extended period, making it a viable option for pulmonary drug delivery. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>Cell viability of (<b>A</b>) A549 alveolar and (<b>B</b>) CFBE bronchial epithelial cells after 1-h treatments with KTP raw and the developed F0, F1 samples measured by impedance. Values are presented as means ± SD, <span class="html-italic">n</span> = 5–6. Statistical analysis: ANOVA followed by Dunett’s test. *** <span class="html-italic">p</span> &lt; 0.001 compared to the control group. C, control; KTP, ketoprofen raw; TX-100, Triton X-100 detergent.</p> Full article ">Figure 2
<p>Permeability of KTP raw, F0, and F1 DPIs (50 μg/mL KTP concentration in the donor compartment) across the co-culture model of (<b>A</b>) alveolar epithelial cells and (<b>B</b>) bronchial epithelial cells after 30- and 60-min assay time. Values are presented as means ± SD, <span class="html-italic">n</span> = 4. Statistical analysis: ANOVA followed by Dunett’s test. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">Figure 3
<p>Evaluation of the tightness of the A549 alveolar and the CFBE bronchial epithelial co-culture models after the KTP raw, F0, and F1 DPIs permeability experiment by (<b>A</b>,<b>C</b>) transepithelial electrical resistance (TEER) measurement and the (<b>B</b>,<b>D</b>) permeability of fluorescein and albumin marker molecules. Values are presented as means ± SD, <span class="html-italic">n</span> = 4. Statistical analysis: ANOVA followed by Dunett’s test.</p> Full article ">Figure 4
<p>Validation of barrier integrity in A549 alveolar epithelial and CFBE bronchial epithelial cell layers after permeability assays with KTP raw, F0, and F1. Immunostaining for (<b>A</b>) β-catenin and (<b>B</b>) zonula occludens protein-1. Cyan: cell nuclei; red: junctional proteins; scale bar: 20 μm. C, control; KTP, ketoprofen raw.</p> Full article ">Figure 5
<p>Laser diffraction results of particle size (D[0.5] (µm)) and particle size distribution (Span) for sample F1 (freshly prepared; F1—0m, and after one month; F1—1m and 3 months; F1—3m of storage). Results are presented as means ± SD, <span class="html-italic">n</span> = 3., **** <span class="html-italic">p</span> &lt; 0.0001 significantly different.</p> Full article ">Figure 6
<p>SEM images of F1 sample at (<b>a</b>) 0 months, (<b>b</b>) 1 month, and (<b>c</b>) 3 months.</p> Full article ">Figure 7
<p>DSC thermal analysis of (<b>A</b>) raw ketoprofen (KTP) and the physical mixture (PM), and (<b>B</b>) sample F1, analyzed as a freshly prepared sample (F1—0m) and after 1 (F1—1m) and 3 (F1—3m) months of storage.</p> Full article ">Figure 7 Cont.
<p>DSC thermal analysis of (<b>A</b>) raw ketoprofen (KTP) and the physical mixture (PM), and (<b>B</b>) sample F1, analyzed as a freshly prepared sample (F1—0m) and after 1 (F1—1m) and 3 (F1—3m) months of storage.</p> Full article ">Figure 8
<p>XRPD structural analysis of (<b>A</b>) raw ketoprofen (KTP) and the physical mixture (PM), and (<b>B</b>) sample F1, analyzed as a freshly prepared sample (F1—0m) and after 1 (F1—1m) and 3 (F1—3m) months of storage. The circles highlight the characteristic peaks of KTP in the raw drug, physical mixture, as well as fresh and stored formulations.</p> Full article ">Figure 9
<p>In vitro aerodynamic characteristics (MMAD, FPF, and EF) of sample F1 were analyzed as a freshly prepared sample (F1—0m), and after 1 (F1—1m) and 3 (F1—3m) months of storage, at a flow rate of 60 L/min. Data are presented as means ± SD (<span class="html-italic">n</span> = 3 independent measurements). Statistical significance is indicated, **** <span class="html-italic">p</span> &lt; 0.0001 significantly different.</p> Full article ">Figure 10
<p>In vitro aerodynamic distribution of the F1 sample was analyzed as a freshly prepared sample (F1—0m), and after 1 (F1—1m) and 3 (F1—3m) months of storage, at a flow rate of 60 L/min. Data are presented as means ± SD (<span class="html-italic">n</span> = 3 independent measurements). Statistical significance is indicated as follows: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p> Full article ">Figure 11
<p>In vitro release study of raw KTP, and spray-dried sample (F1) was analyzed as a freshly prepared sample (F1—0m) and after 1 (F1—1m) and 3 (F1—3m) months of storage, in simulated lung media (SLM). Results are expressed as mean ± SD (<span class="html-italic">n</span> = 3 independent measurements).</p> Full article ">

attachment

Supplementary material:
Supplementary File 1 (ZIP, 240 KiB)

28 pages, 1249 KiB  

Open AccessSystematic Review

Technological Advances for Gait and Balance in Normal Pressure Hydrocephalus: A Systematic Review

by Alessandro Zampogna, Martina Patera, Marco Falletti, Giulia Pinola, Francesco Asci and Antonio Suppa

Bioengineering 2025, 12(2), 135; https://doi.org/10.3390/bioengineering12020135 - 30 Jan 2025

Abstract

Normal pressure hydrocephalus (NPH) is a recognized cause of reversible cognitive and motor decline, with gait and balance impairments often emerging early. Technologies providing gait and balance measures can aid in early detection, diagnosis, and prognosis of the disease. This systematic review comprehensively [...] Read more.

Normal pressure hydrocephalus (NPH) is a recognized cause of reversible cognitive and motor decline, with gait and balance impairments often emerging early. Technologies providing gait and balance measures can aid in early detection, diagnosis, and prognosis of the disease. This systematic review comprehensively discusses previous studies on the instrumental assessment of gait and balance in NPH. A PubMed search following PRISMA guidelines identified studies published between 2000 and 2024 that used laboratory instruments to assess gait and balance in NPH. Studies underwent quality assessment for internal, statistical, and external validity. Methodological details such as motor tasks, instruments, analytical approaches, and main findings were summarized. Overall, this review includes 41 studies on gait and 17 on balance, most of which used observational, cross-sectional designs. These studies employed various tools, such as pressure-sensitive platforms, optoelectronic motion-capture systems, and wearable inertial sensors. Significant differences in kinematic measures of gait and balance have been found in NPH patients compared to healthy controls and individuals with other neurological conditions. Finally, this review explores potential pathophysiological mechanisms underlying the kinematic changes in gait and balance in NPH and emphasizes the absence of longitudinal data, which hinders drawing definitive conclusions for prognostic purposes. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>Flow diagram for the selection of studies.</p> Full article ">Figure 2
<p>Consistency of studies on the main findings of gait changes when comparing patients with normal pressure hydrocephalus and healthy subjects. Note: measures varied across studies; the main methods and findings of the 17 selected articles focused on the objective assessment of balance through laboratory instruments are summarized.</p> Full article ">Figure 3
<p>Consistency of studies on the main findings of balance changes when comparing patients with normal pressure hydrocephalus and healthy subjects. Note: measures varied across studies; therefore, the displayed counts differ from the total studies.</p> Full article ">

15 pages, 6427 KiB  

Open AccessArticle

Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT Projections

by Shafiya Sabah and Salam Dhou

Appl. Syst. Innov. 2025, 8(1), 20; https://doi.org/10.3390/asi8010020 - 26 Jan 2025

Abstract

Respiratory motion serves as a major challenge during treatment of lung cancer patients using radiotherapy. In this work, an image-based method is presented to extract a respiratory signal directly from Cone Beam CT (CBCT) projections. A dense optical-flow method is used to acquire [...] Read more.

Respiratory motion serves as a major challenge during treatment of lung cancer patients using radiotherapy. In this work, an image-based method is presented to extract a respiratory signal directly from Cone Beam CT (CBCT) projections. A dense optical-flow method is used to acquire motion vectors between successive projections in each dataset, followed by the extraction of the dominant motion pattern by application of linear kernel Principal Component Analysis (PCA). The effectiveness of the method was tested on three patient datasets and the extracted breathing signal was compared to a ground-truth signal. The average phase shift was observed to be 1.936 ± 0.734 for patient 1, 1.185 ± 0.781 for patient 2 and 1.537 ± 0.93 for patient 3. Moreover, a 4D CBCT image was reconstructed, considering the respiratory signal extracted, using the proposed method, and compared to that reconstructed considering the ground-truth respiratory signal. Results showed that a minimal difference was found between the image reconstructed using the proposed method and the ground-truth in terms of clarity, motion artifacts and edge sharpness. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>Methods for respiratory motion management for radiotherapy.</p> Full article ">Figure 2
<p>Respiratory phase-sorting process. The red arrows represent the respiratory phases exhibited by the respective projections.</p> Full article ">Figure 3
<p>(<b>a</b>) Extracted breathing signal for patient 1 compared with ground-truth (diaphragm position). (<b>b</b>) Phase-sorting plot for patient 1 dataset.</p> Full article ">Figure 4
<p>Extracted signal (left) and the corresponding phase-sorting plot (right) for patient 2: (<b>a</b>,<b>b</b>) projections 1367 to 1667, (<b>c</b>,<b>d</b>) projections 1967 to 2267.</p> Full article ">Figure 5
<p>Extracted breathing signal vs. all marker positions for patient 3.</p> Full article ">Figure 6
<p>Phase-sorting plot of extracted signal for patient 3: (<b>a</b>) projections 1 to 300, (<b>b</b>) projections 300 to 600, (<b>c</b>) projections 600 to 900, (<b>d</b>) projections 1500 to 1800.</p> Full article ">Figure 7
<p>Coronal slices from the reconstructed images of the phantom dataset. (<b>a</b>) Three-dimensional image, where all projections belonging to all breathing phases were used for reconstruction, (<b>b</b>) projections belonging to phase 1 only, used for reconstruction considering the ground-truth breathing signal, (<b>c</b>) projections belonging to phase 1 only, used for reconstruction considering the breathing signal extracted using the proposed method, and (<b>d</b>) an image resulting from subtracting (<b>c</b>) from (<b>b</b>). The yellow vertical line in (<b>a</b>) represents the profile considered to evaluate the sharpness of the edges in the compared images.</p> Full article ">Figure 8
<p>Edge profile along diaphragm of reconstructed slice of phantom dataset 2.</p> Full article ">

10 pages, 2470 KiB  

Open AccessArticle

Improving Workplace Safety and Health Through a Rapid Ergonomic Risk Assessment Methodology Enhanced by an Artificial Intelligence System

by Adrian Ispășoiu, Ioan Milosan and Camelia Gabor

Appl. Syst. Innov. 2024, 7(6), 103; https://doi.org/10.3390/asi7060103 - 28 Oct 2024

Viewed by 1121

Abstract

The comfort of a worker while performing any activity is extremely important. If that activity extends beyond a person’s capacity to withstand physical and psychological stress, the worker may suffer from both physical and mental ailments. Over time, if the stress persists, these [...] Read more.

The comfort of a worker while performing any activity is extremely important. If that activity extends beyond a person’s capacity to withstand physical and psychological stress, the worker may suffer from both physical and mental ailments. Over time, if the stress persists, these conditions can become chronic diseases and can even be the cause of workplace accidents. In this research, a methodology was developed for the rapid assessment of ergonomic risks and for calculating the level of ergonomic comfort in the workplace. This methodology uses artificial intelligence through a specific algorithm and takes into account a number of factors that, when combined, can have a significant impact on workers. To achieve a more accurate simulation of a work situation or to evaluate an ongoing work situation, and to significantly correlate these parameters, we used logarithmic calculation formulas. To streamline the process, we developed software that performs these calculations, conducts a rapid assessment of ergonomic risks, estimates a comfort level, and proposes possible measures to mitigate the risks and effects on workers. To assist in diagnosing the work situation, we used a neural network with five neurons in the input layer, one hidden layer, and two neurons in the output layer. As a result, most work situations, in any industrial field, can be quickly analyzed and evaluated using this methodology. The use of this new analysis and diagnosis tool, implemented through this new research technology, is beneficial for employers and workers. Moreover, through further developments of this methodology, achieved by increasing the number of relevant input parameters for ergonomics and integrating advanced artificial intelligence systems, we aim to provide high precision in assessing ergonomic risk and calculating the level of ergonomic comfort. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>Screenshot from the ERGO RERA software application—AI V1.27.00.</p> Full article ">Figure 2
<p>The steps of the RERA methodology.</p> Full article ">Figure 3
<p>Ergonomic risk levels obtained from the workers (L1–L8) for the studied activity.</p> Full article ">Figure 4
<p>A graphic representation of the values of the parameters of the ergonomic risk factors for the polishing operation (deburring, smoothing) for each worker.</p> Full article ">

attachment

Supplementary material:
Supplementary File 1 (ZIP, 329 KiB)

13 pages, 1315 KiB  

Open AccessArticle

An Effective DNA Methylation Biomarker Screening Mechanism for Amyotrophic Lateral Sclerosis (ALS) Based on Comorbidities and Gene Function Analysis

by Cing-Han Yang, Jhen-Li Huang, Li-Kai Tsai, David Taniar and Tun-Wen Pai

Bioengineering 2024, 11(10), 1020; https://doi.org/10.3390/bioengineering11101020 - 12 Oct 2024

Viewed by 1131

Abstract

This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. [...] Read more.

This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. We also applied word2vec to these two clinical diagnostic medical databases to measure similarities between ALS and other similar diseases and evaluated the statistical assessment of the odds ratio to discover significant comorbidities for ALS subjects. Important and representative DNA methylation biomarker candidates could be effectively selected by cross-comparing similar diseases to ALS, comorbidity-related genes, and differentially expressed methylation loci for ALS subjects. The screened epigenomic and comorbidity-related biomarkers were clustered based on their genetic functions. The candidate DNA methylation biomarkers associated with ALS were comprehensively discovered. Gene ontology annotations were then applied to analyze and cluster the candidate biomarkers into three different groups based on gene function annotations. The results showed that a potential testing kit for ALS detection can be composed of SOD3, CACNA1H, and ERBB4 for effective early screening of ALS using blood samples. By developing an effective DNA methylation biomarker screening mechanism, early detection and prophylactic treatment of high-risk ALS patients can be achieved. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>Flow chart showing the pipeline of exploring functional representative biomarkers by integrating primary biomarkers from DNA methylation analysis and secondary biomarkers from related comorbidity patterns.</p> Full article ">Figure 2
<p>Pipeline operations to discover primary DMP biomarkers by using GEO methylation profiling data.</p> Full article ">Figure 3
<p>Functional annotation results of the nine candidate biomarkers. The horizontal axis represents the number of genes corresponding to each function. The purple, yellow and green legends were the corresponding annotation results of candidate biomarkers in three main ontologies: biological processes, cellular components, and molecular functions. The red legend represents the annotation results of KEGG biological pathways.</p> Full article ">

attachment

Supplementary material:
Supplementary File 1 (ZIP, 91 KiB)

18 pages, 3454 KiB  

Open AccessArticle

“BrainHeart”: Pilot Study on a Novel Application for Elderly Well-Being Based on Mindfulness Acceptance and Commitment Therapy

by Roberta Bruschetta, Desiree Latella, Caterina Formica, Simona Campisi, Chiara Failla, Flavia Marino, Serena Iacono Isidoro, Fabio Mauro Giambò, Lilla Bonanno, Antonio Cerasa, Angelo Quartarone, Silvia Marino, Giovanni Pioggia, Rocco Salvatore Calabrò and Gennaro Tartarisco

Bioengineering 2024, 11(8), 787; https://doi.org/10.3390/bioengineering11080787 - 3 Aug 2024

Viewed by 1574

Abstract

The rising prevalence of mental illness is straining global mental health systems, particularly affecting older adults who often face deteriorating physical health and decreased autonomy and quality of life. Early detection and targeted rehabilitation are crucial in mitigating these challenges. Mindfulness acceptance and [...] Read more.

The rising prevalence of mental illness is straining global mental health systems, particularly affecting older adults who often face deteriorating physical health and decreased autonomy and quality of life. Early detection and targeted rehabilitation are crucial in mitigating these challenges. Mindfulness acceptance and commitment therapy (ACT) holds promise for enhancing motivation and well-being among the elderly, although delivering such psychological interventions is hindered by limited access to services, prompting exploration of remote delivery options like mobile applications. In this paper, we introduce the BrainHeart App (v.1.1.8), a mobile application tailored to improve physical and mental well-being in seniors. The app features a 10-day ACT program and other sections promoting healthy lifestyle. In a pilot study involving twenty participants, individuals engaged in daily mental exercises for 10 days using the app. Clinical evaluations, including assessments of psychological flexibility, overall cognitive profile, mindfulness disposition, cognitive fusion, and heart rate collected with Polar H10, were conducted at baseline (T0) and one month post-intervention (T1). Analysis revealed significant improvements in almost all neuropsychological scores, with high usability reported (system usability scale average score: 82.3 ± 9.31). Additionally, a negative correlation was found between usability and experiential avoidance (r = −0.51; p = 0.026), and a notable difference in heart rate was observed between baseline and post-intervention (F-value = 3.06; p-value = 0.09). These findings suggest that mindfulness-ACT exercises delivered via the BrainHeart App can enhance the well-being of elderly individuals, highlighting the potential of remote interventions in addressing mental health needs in this population. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>Sections of BrainHeart mobile application to promote overall psycho-physical well-being.</p> Full article ">Figure 2
<p>Example of nutrition tips and setting of dietary habits.</p> Full article ">Figure 3
<p>Example of personalized physical exercises program.</p> Full article ">Figure 4
<p>Example of data collection through the integration of a Polar H10 chest heat rate sensor.</p> Full article ">Figure 5
<p>Example of meditation exercise included in the mindfulness section.</p> Full article ">Figure 6
<p>The Hexaflex Model.</p> Full article ">Figure 7
<p>A participant wearing a T-shirt equipped with a Polar H10 sensor for measuring heart rate variability interacts with the BrainHeart application using her smartphone.</p> Full article ">Figure 8
<p>Bar plot of the assessed scores at T0 and T1 with significance level from paired-samples Wilcoxon test. Legend: ns = <span class="html-italic">p</span> &gt; 0.05, ** = <span class="html-italic">p</span> ≤ 0.01, *** = <span class="html-italic">p</span> ≤ 0.001. Prior to visualization, all scores were normalized through logarithmic transformation.</p> Full article ">Figure 9
<p>Scatter plot between system usability scale and experiential avoidance variation (T1-T0) with Spearman correlation coefficient and <span class="html-italic">p</span>-value.</p> Full article ">

attachment

Supplementary material:
Supplementary File 1 (ZIP, 106 KiB)

12 pages, 1266 KiB  

Open AccessArticle

Stratification of Older Adults According to Frailty Status and Falls Using Gait Parameters Explored Using an Inertial System

by Marta Neira Álvarez, Elisabet Huertas-Hoyas, Robert Novak, Ana Elizabeth Sipols, Guillermo García-Villamil-Neira, M. Cristina Rodríguez-Sánchez, Antonio J. Del-Ama, Luisa Ruiz-Ruiz, Sara García De Villa and Antonio R. Jiménez-Ruiz

Appl. Sci. 2024, 14(15), 6704; https://doi.org/10.3390/app14156704 - 1 Aug 2024

Cited by 1 | Viewed by 986

Abstract

Background: The World Health Organization recommends health initiatives focused on the early detection of frailty and falls. Objectives: 1—To compare clinical characteristics, functional performance and gait parameters (estimated with the G-STRIDE inertial sensor) between different frailty groups in older adults with and without [...] Read more.

Background: The World Health Organization recommends health initiatives focused on the early detection of frailty and falls. Objectives: 1—To compare clinical characteristics, functional performance and gait parameters (estimated with the G-STRIDE inertial sensor) between different frailty groups in older adults with and without falls. 2—To identify variables that stratify participants according to frailty status and falls. 3—To verify the sensitivity, specificity and accuracy of the model that stratifies participants according to frailty status and falls. Methods: Observational, multicenter case-control study. Participants, adults over 70 years with and without falls were recruited from two outpatient clinics and three nursing homes from September 2021 to March 2022. Clinical variables and gait parameters were gathered using the G-STRIDE inertial sensor. Random Forest regression was applied to stratify participants. Results: 163 participants with a mean age of 82.6 ± 6.2 years, of which 118 (72%) were women, were included. Significant differences were found in all gait parameters (both conventional assessment and G-STRIDE evaluation). A hierarchy of factors contributed to the risk of frailty and falls. The confusion matrix and the performance metrics demonstrated high accuracy in classifying participants. Conclusions: Gait parameters, particularly those assessed by G-STRIDE, are effective in stratifying individuals by frailty status and falls. These findings underscore the importance of gait analysis in early intervention strategies. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>G-STRIDE device attached to a participant’s foot.</p> Full article ">Figure 2
<p>Relative variable importance in Random Forest regression.</p> Full article ">Figure 3
<p>Confusion matrix.</p> Full article ">

14 pages, 7338 KiB  

Open AccessArticle

pH-Dependent Morphology of Copper (II) Oxide in Hydrothermal Process and Their Photoelectrochemical Application for Non-Enzymatic Glucose Biosensor

by Trung Tin Tran, Anh Hao Huynh Vo, Thien Trang Nguyen, Anh Duong Nguyen, My Hoa Huynh Tran, Viet Cuong Tran and Trung Nghia Tran

Appl. Sci. 2024, 14(13), 5688; https://doi.org/10.3390/app14135688 - 29 Jun 2024

Cited by 1 | Viewed by 1437

Abstract

In this study, we investigated the influence of pH on the hydrothermal synthesis of copper (II) oxide CuO nanostructures with the aim of tuning their morphology. By varying the pH of the reaction medium, we successfully produced CuO nanostructures with three distinct morphologies [...] Read more.

In this study, we investigated the influence of pH on the hydrothermal synthesis of copper (II) oxide CuO nanostructures with the aim of tuning their morphology. By varying the pH of the reaction medium, we successfully produced CuO nanostructures with three distinct morphologies including nanoparticles, nanorods, and nanosheets according to the pH levels of 4, 7, and 12, respectively. The observed variations in surface morphology are attributed to fluctuations in growth rates across different crystal facets, which are influenced by the presence of intermediate species within the reaction. This report also compared the structural and optical properties of the synthesized CuO nanostructures and explored their potential for photoelectrochemical glucose sensing. Notably, CuO nanoparticles and nanorods displayed exceptional performance with calculated limits of detection of 0.69 nM and 0.61 nM, respectively. Both of these morphologies exhibited a linear response to glucose within their corresponding concentration ranges (3–20 nM and 20–150 nM). As a result, CuO nanorods appear to be a more favorable photoelectrochemical sensing method because of the large surface area as well as the lowest solution resistance in electroimpedance analysis compared to CuO nanoparticles and nanosheets forms. These findings strongly suggest the promising application of hydrothermal-synthesized CuO nanostructures for ultrasensitive photoelectrochemical glucose biosensors. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>The process synthesis of different morphologies of (<math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math>) materials.</p> Full article ">Figure 2
<p>The PEC sensor system for glucose detection.</p> Full article ">Figure 3
<p>XRD patterns of the synthesized <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> materials.</p> Full article ">Figure 4
<p>SEM images of <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> synthesized at pH values of 4, 7, and 12 corresponding to (<b>a</b>,<b>b</b>) particles, (<b>c</b>,<b>d</b>) rods, and (<b>e</b>,<b>f</b>) sheets morphologies.</p> Full article ">Figure 5
<p>(<b>a</b>) UV−VIS absorbance spectra and appearance of <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NPs, <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NRs, and <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NSs. (<b>b</b>) Tauc plots of <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NPs, <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NRs, and <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NSs.</p> Full article ">Figure 6
<p>XRD pattern of the uncompleted samples after 15 h of hydrothermal process.</p> Full article ">Figure 7
<p>(<b>a</b>) Electrochemical impedance spectroscopy of bare <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> </mrow> </semantics></math> and three morphologies <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> <mo>/</mo> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> nanomaterials without glucose with the inserted equivalent circuit in the low-frequency region. Semicircular shapes in high-frequency Nyquist plots of (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> <mo>/</mo> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NPs, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> <mo>/</mo> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NRs, and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> <mo>/</mo> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NSs electrodes.</p> Full article ">Figure 8
<p>Electrochemical impedance spectroscopy of (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NPs, (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NRs, and (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NSs sprayed on <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> </mrow> </semantics></math> electrodes with and without light irradiation.</p> Full article ">Figure 9
<p>(<b>a</b>) Comparison of photocurrent responses of three types of <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> <mo>/</mo> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> nanostructures to 100 nM glucose at an applied potential of 0.2 V (vs. <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>g</mi> <mo>/</mo> <mi>A</mi> <mi>g</mi> <mi>C</mi> <mi>l</mi> </mrow> </semantics></math>) under 395 nm LED irradiation. Photocurrent responses of (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> <mo>/</mo> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NPs, (<b>c</b>) <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> <mo>/</mo> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NRs, and (<b>d</b>) <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> <mo>/</mo> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NSs electrodes at different glucose concentrations from 0 to 150 nM.</p> Full article ">Figure 10
<p>Corresponding calibration curve of (<b>a</b>) <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> <mo>/</mo> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NPs electrode and (<b>b</b>) <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> <mo>/</mo> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> NRs electrode.</p> Full article ">Figure 11
<p>Mechanism of non−enzymatic glucose sensing on the <math display="inline"><semantics> <mrow> <mi>I</mi> <mi>T</mi> <mi>O</mi> <mo>/</mo> <mi>C</mi> <mi>u</mi> <mi>O</mi> </mrow> </semantics></math> electrode.</p> Full article ">

12 pages, 1503 KiB  

Open AccessArticle

Electromagnetic Imaging of Uniaxial Objects by Two-Step Neural Network

by Wei Chien, Chien-Ching Chiu, Po-Hsiang Chen, Hung-Yu Wu and Eng Hock Lim

Appl. Sci. 2024, 14(13), 5624; https://doi.org/10.3390/app14135624 - 27 Jun 2024

Viewed by 692

Abstract

The integration of electromagnetic imaging technology with the Internet of Things plays an important role in fields as diverse as healthcare, geophysics, and industrial diagnostics. This paper presents a novel two-step neural network architecture to solve the electromagnetic imaging for uniaxial objects which [...] Read more.

The integration of electromagnetic imaging technology with the Internet of Things plays an important role in fields as diverse as healthcare, geophysics, and industrial diagnostics. This paper presents a novel two-step neural network architecture to solve the electromagnetic imaging for uniaxial objects which can be used in the Internet of Things. We incident TM and TE waves to unknown objects and receive the scattered fields. In order to reduce the training difficulty, we first input the gathered scattered field information into a deep convolutional neural network (DCNN) to obtain the preliminary guess. In the second step, we feed the guessed image into the convolutional neural network (CNN) to reconstruct high-resolution images. Our numerical results demonstrate the real-time imaging capability of our proposed two-step method in reconstructing high-contrast scatterers. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>The diagram illustrates the schematic of electromagnetic imaging and the IoT.</p> Full article ">Figure 2
<p>Schematic of dielectric scatterer.</p> Full article ">Figure 3
<p>Deep convolutional neural network architecture. <math display="inline"><semantics> <mi>M</mi> </semantics></math> denotes the receivers, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mi>i</mi> </msub> </mrow> </semantics></math> denotes the transmitter point, and N denotes the number of split units.</p> Full article ">Figure 4
<p>Convolution neural network architecture.</p> Full article ">Figure 5
<p>Reconstruction results of relative permittivity between 1 and 1.5 by TM case with 20% noise. (<b>a</b>) Ground truth; (<b>b</b>) reconstructed image.</p> Full article ">Figure 6
<p>Reconstruction results of relative permittivity between 1 and 1.5 by TE case with 20% noise. (<b>a</b>) Ground truth; (<b>b</b>) reconstructed image.</p> Full article ">Figure 7
<p>Reconstruction outcome of relative permittivity range from 1.5 to 2 by TM case with 5% noise. (<b>a</b>) Ground truth; (<b>b</b>) reconstructed image.</p> Full article ">Figure 8
<p>Reconstruction outcome of relative permittivity range from 1.5 to 2 by TE case with 5% noise. (<b>a</b>) Ground truth; (<b>b</b>) reconstructed image.</p> Full article ">Figure 9
<p>Reconstruction results of dielectric distribution between 7.5 and 8 by TM case with 5% noise. (<b>a</b>) Ground truth; (<b>b</b>) reconstructed image.</p> Full article ">Figure 10
<p>Reconstruction results of dielectric distribution between 7.5 and 8 by TE case with 5% noise. (<b>a</b>) Ground truth; (<b>b</b>) reconstructed image.</p> Full article ">Figure 11
<p>The schematic diagram of FoamDielExt.</p> Full article ">Figure 12
<p>Reconstruction outcome by the experimental data. (<b>a</b>) TM case; (<b>b</b>) TE case.</p> Full article ">

attachment

Supplementary material:
Supplementary File 1 (ZIP, 204 KiB)

16 pages, 4054 KiB  

Open AccessArticle

Investigating the Effect of Cyclodextrin Nanosponges and Cyclodextrin-Based Hydrophilic Polymers on the Chemical Pharmaceutical and Toxicological Profile of Al(III) and Ga(III) Complexes with 5-Hydroxyflavone

by Claudiu Radu, Andreea Alexandra Olteanu, Corina Cristina Aramă, Mirela Mihăilă and Valentina Uivaroși

Appl. Sci. 2024, 14(13), 5441; https://doi.org/10.3390/app14135441 - 23 Jun 2024

Viewed by 1072

Abstract

In the present study, the complexes of aluminum and gallium with 5-hydroxyflavone were evaluated for their interaction with cyclodextrin polymers, as well as for the pharmacological effect of their inclusion. The cyclodextrin polymers were synthesized using diphenylcarbonate as a crosslinking agent, resulting in [...] Read more.

In the present study, the complexes of aluminum and gallium with 5-hydroxyflavone were evaluated for their interaction with cyclodextrin polymers, as well as for the pharmacological effect of their inclusion. The cyclodextrin polymers were synthesized using diphenylcarbonate as a crosslinking agent, resulting in a lipophilic nanosponge (DPCNS), and pyromellitic dianhydride, resulting in a hydrophilic polymer (PMDACD). The inclusion complexes were synthesized and characterized via IR spectrometry and thermal analysis. The effect on the solubility of the metal complexes was also studied, where the hydrophobic nanosponge did not lead to an increase in solubility, but on the contrary, in the case of Al, it decreased; meanwhile, in the case of the hydrophilic polymer, the solubility of the metal complexes increased with the amount of polymer added. The cytostatic effect of inclusion complexes was investigated on two cell lines with different localizations, human colon adenocarcinoma (LoVo) and human ovarian adenocarcinoma (SKOV-3). The cytostatic efficacy is increased compared to simple complexes with efficacy on LoVo cells. Compared between the two metals, gallium complexes proved to be more active, with the efficacy of gallium complexes with the PMDACD being approximately the same as that of cisplatin, an antitumor agent used in therapy. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>The general chemical structure of flavonoids.</p> Full article ">Figure 2
<p>Structure of 5-hydroxyflavone.</p> Full article ">Figure 3
<p>FT-IR spectra of (<b>a</b>) Al-5HF-PMDACD1 and (<b>b</b>) Ga-5HF-PMDACD1.</p> Full article ">Figure 4
<p>DSC curves of Ga-5HF (red), Al-5HF (black), Ga-5HF-DPCNS2 (blue), Al-5HF-DPCNS2 (green), Al-5HF-PMDACD1 (purple) and Ga-5HF-PMDACD1 (brown).</p> Full article ">Figure 5
<p>Concentration of the metal complexes in water, alone, and in the presence of cyclodextrin nanosponges.</p> Full article ">Figure 6
<p>The variation of metal complexes concentration in the presence of increasing quantities of DPCNS.</p> Full article ">Figure 7
<p>The variation in metal complex concentrations in the presence of increasing quantities of PMDANS.</p> Full article ">Figure 8
<p>The impact of the tested compounds on the viability of normal HUVEC cells after (<b>a</b>) 24 h of treatment and after (<b>b</b>) 48 h of treatment.</p> Full article ">Figure 9
<p>The impact of the tested compounds on the viability of LoVo colon cancer cells after (<b>a</b>) 24 h of treatment and (<b>b</b>) 48 h of treatment.</p> Full article ">Figure 10
<p>The impact of the tested compounds on the viability of SKOV-3 ovary cancer cells after (<b>a</b>) 24 h of treatment and (<b>b</b>) 48 h of treatment.</p> Full article ">Figure 11
<p>Effect of the analyzed compounds on the apoptotic process of the HUVEC normal cells after (<b>a</b>) 24 h of treatment and (<b>b</b>) 48 h of treatment.</p> Full article ">Figure 12
<p>Effect of the analyzed compounds on the apoptotic process of the LoVo colon cancer cells after (<b>a</b>) 24 h of treatment and (<b>b</b>) 48 h of treatment.</p> Full article ">Figure 13
<p>Effect of the analyzed compounds on the apoptotic process of the SKOV-3 ovarian cancer cells after (<b>a</b>) 24 h of treatment and (<b>b</b>) 48 h of treatment.</p> Full article ">

18 pages, 3164 KiB  

Open AccessArticle

Cough Detection Using Acceleration Signals and Deep Learning Techniques

by Daniel Sanchez-Morillo, Diego Sales-Lerida, Blanca Priego-Torres and Antonio León-Jiménez

Electronics 2024, 13(12), 2410; https://doi.org/10.3390/electronics13122410 - 20 Jun 2024

Viewed by 1511

Abstract

Cough is a frequent symptom in many common respiratory diseases and is considered a predictor of early exacerbation or even disease progression. Continuous cough monitoring offers valuable insights into treatment effectiveness, aiding healthcare providers in timely intervention to prevent exacerbations and hospitalizations. Objective [...] Read more.

Cough is a frequent symptom in many common respiratory diseases and is considered a predictor of early exacerbation or even disease progression. Continuous cough monitoring offers valuable insights into treatment effectiveness, aiding healthcare providers in timely intervention to prevent exacerbations and hospitalizations. Objective cough monitoring methods have emerged as superior alternatives to subjective methods like questionnaires. In recent years, cough has been monitored using wearable devices equipped with microphones. However, the discrimination of cough sounds from background noise has been shown a particular challenge. This study aimed to demonstrate the effectiveness of single-axis acceleration signals combined with state-of-the-art deep learning (DL) algorithms to distinguish intentional coughing from sounds like speech, laugh, or throat noises. Various DL methods (recurrent, convolutional, and deep convolutional neural networks) combined with one- and two-dimensional time and time–frequency representations, such as the signal envelope, kurtogram, wavelet scalogram, mel, Bark, and the equivalent rectangular bandwidth spectrum (ERB) spectrograms, were employed to identify the most effective approach. The optimal strategy, which involved the SqueezeNet model in conjunction with wavelet scalograms, yielded an accuracy and precision of 92.21% and 95.59%, respectively. The proposed method demonstrated its potential for cough monitoring. Future research will focus on validating the system in spontaneous coughing of subjects with respiratory diseases under natural ambulatory conditions. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>An example of the accelerometer signal recorded in one of the experimental sessions. The different types of signals recorded over time in each experiment (cough, speech, laugh, and clearing voice noise) are labeled.</p> Full article ">Figure 2
<p>Detail of the prototype for signal acquisition.</p> Full article ">Figure 3
<p>Deep learning models, features, and time–frequency representations used in the study.</p> Full article ">Figure 4
<p>LSTM model architecture. FC: Fully connected.</p> Full article ">Figure 5
<p>Architecture of the CNN model.</p> Full article ">Figure 6
<p>The architecture of the VGGish model.</p> Full article ">Figure 7
<p>Examples of ERB, mel, and Bark spectrograms for cough and non-cough signals.</p> Full article ">Figure 8
<p>Examples of mel, kurtogram, wavelet scalogram, and signal envelope for cough and non-cough signals.</p> Full article ">

46 pages, 4394 KiB  

Open AccessArticle

Empowering Healthcare: A Comprehensive Guide to Implementing a Robust Medical Information System—Components, Benefits, Objectives, Evaluation Criteria, and Seamless Deployment Strategies

by Ana-Maria Ștefan, Nicu-Răzvan Rusu, Elena Ovreiu and Mihai Ciuc

Appl. Syst. Innov. 2024, 7(3), 51; https://doi.org/10.3390/asi7030051 - 14 Jun 2024

Viewed by 8633

Abstract

In the ever-evolving landscape of healthcare, the implementation of a robust medical information system stands as a transformative endeavor. This article serves as a comprehensive guide, delineating the intricate steps involved in deploying an effective medical information system. Delving into the main components [...] Read more.

In the ever-evolving landscape of healthcare, the implementation of a robust medical information system stands as a transformative endeavor. This article serves as a comprehensive guide, delineating the intricate steps involved in deploying an effective medical information system. Delving into the main components that constitute this innovative system, we explore its fundamental architecture and how each element contributes to seamless information flow. The benefits of adopting a medical information system are highlighted, emphasizing improved patient care, streamlined processes, and enhanced decision making for healthcare professionals. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>Structure of a medical information system.</p> Full article ">Figure 2
<p>Components of an information system.</p> Full article ">Figure 3
<p>Phases of implementing an information system.</p> Full article ">Figure 4
<p>Use case diagram for a medical information system.</p> Full article ">Figure 5
<p>Activity diagram for medical information system.</p> Full article ">Figure 6
<p>Data flow diagram for an appointment booking in a medical information system.</p> Full article ">Figure 7
<p>Class diagram for an appointment booking in a medical information system.</p> Full article ">Figure 8
<p>Package diagram for an appointment booking in a medical information system.</p> Full article ">Figure 9
<p>State diagram for an appointment booking in a medical information system.</p> Full article ">Figure 10
<p>ERD for an appointment booking in a medical information system.</p> Full article ">Figure 11
<p>Component diagram for an appointment booking in a medical information system.</p> Full article ">Figure 12
<p>Sequence diagram for an appointment booking in a medical information system.</p> Full article ">Figure 13
<p>Example of a MIS implemented for a medical institution [<a href="#B12-asi-07-00051" class="html-bibr">12</a>].</p> Full article ">

17 pages, 7019 KiB  

Open AccessArticle

Colorectal Polyp Detection Model by Using Super-Resolution Reconstruction and YOLO

by Shaofang Wang, Jun Xie, Yanrong Cui and Zhongju Chen

Electronics 2024, 13(12), 2298; https://doi.org/10.3390/electronics13122298 - 12 Jun 2024

Cited by 3 | Viewed by 1452

Abstract

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Colonoscopy is the primary method to prevent CRC. However, traditional polyp detection methods face problems such as low image resolution and the possibility of missing polyps. In recent years, deep learning [...] Read more.

Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. Colonoscopy is the primary method to prevent CRC. However, traditional polyp detection methods face problems such as low image resolution and the possibility of missing polyps. In recent years, deep learning techniques have been extensively employed in the detection of colorectal polyps. However, these algorithms have not yet addressed the issue of detection in low-resolution images. In this study, we propose a novel YOLO-SRPD model by integrating SRGAN and YOLO to address the issue of low-resolution colonoscopy images. Firstly, the SRGAN with integrated ACmix is used to convert low-resolution images to high-resolution images. The generated high-resolution images are then used as the training set for polyp detection. Then, the C3_Res2Net is integrated into the YOLOv5 backbone to enhance multiscale feature extraction. Finally, CBAM modules are added before the prediction head to enhance attention to polyp information. The experimental results indicate that YOLO-SRPD achieves a mean average precision (mAP) of 94.2% and a precision of 95.2%. Compared to the original model (YOLOv5), the average accuracy increased by 1.8% and the recall rate increased by 5.6%. These experimental results confirm that YOLO-SRPD can address the low-resolution problem during colorectal polyp detection and exhibit exceptional robustness. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Figure 1

Figure 1
<p>Illustration of the polyp detection architecture.</p> Full article ">Figure 2
<p>Structure of improved SRGAN.</p> Full article ">Figure 3
<p>Structure of ACmix.</p> Full article ">Figure 4
<p>Improved YOLOv5-based polyp detection framework.</p> Full article ">Figure 5
<p>C3_Res2net Block (<b>a</b>) and Res2Net Module (s = 4) (<b>b</b>).</p> Full article ">Figure 6
<p>CBAM attention mechanism module.</p> Full article ">Figure 7
<p>Comparison of images before (<b>a</b>) and after (<b>b</b>) SR reconstruction.</p> Full article ">Figure 8
<p>Visualization results before and after colon polyp image reconstruction.</p> Full article ">Figure 9
<p>PNSR training.</p> Full article ">Figure 10
<p>SSIM training curve.</p> Full article ">Figure 11
<p>Comparison curve of different models.</p> Full article ">Figure 12
<p>The replacement location of the C3 module.</p> Full article ">Figure 13
<p>Training results curve of YOLO-SRPD.</p> Full article ">Figure 14
<p>Examples of polyp detection.</p> Full article ">

22 pages, 18402 KiB  

Open AccessArticle

Preliminary Failure Analyses of Loaded Hot Water Bottles

by Joseph Towler, Mohamed Baraya, Ziying Ran, Adel Alshammari, Syead Arif, Mohammad Desai, Sasidharan Palanivel, Rosti Readioff and Ahmed Abass

Appl. Sci. 2024, 14(11), 4427; https://doi.org/10.3390/app14114427 - 23 May 2024

Viewed by 1216

Abstract

Hot water bottles are widely utilised for their therapeutic advantages, such as relieving muscle tension and imparting warmth. However, the increasing frequency and potential risks associated with bursting or failure necessitate a detailed examination of the contributing factors as their failure is not [...] Read more.

Hot water bottles are widely utilised for their therapeutic advantages, such as relieving muscle tension and imparting warmth. However, the increasing frequency and potential risks associated with bursting or failure necessitate a detailed examination of the contributing factors as their failure is not fully understood in a scientific manner. With the apparent lack of analysis of hot water bottles in the literature, this study employs, for the first time, a dual methodology involving finite-element (FE) analysis conducted in ABAQUS and experimental validation to systematically investigate the underlying mechanisms leading to failure incidents. Through FE modelling and analysis, the stress and strain distribution within typical hot water bottles is modelled under compression loading conditions, facilitating the identification of vulnerable areas prone to failure. Experimental validation encompasses uniaxial loading compression tests on distinct specimens, generating load–displacement curves that elucidate material responses to compressive forces and highlight variations in load-bearing capacities. The study explores diverse failure modes, attributing them to stress concentration at geometric transitions and contact regions. Stress–strain curves contribute valuable insights into material characteristics, with ultimate stress values as crucial indicators of resistance to deformation and rupture. The FE analysis simulation results visualise deformation patterns and stress concentration zones. The findings illustrate that the highest stress concentration areas exist in the internal boundary of hot water bottles near the neck and cap region. This is experimentally confirmed through the bursting failures of four samples, with three failures occurring in this specific region. The findings support the guidance that users should avoid sleeping with a hot water bottle as it may fail under compression if they lay on top of it. Meanwhile, this result guides manufacturers to strengthen the weak areas of hot water bottles around the nicks and edges. This study significantly enhances our understanding of hot water bottle mechanics, thereby guiding design practice to improve overall performance and user safety. In summary, hot water bottles are commonly used but have not been investigated scientifically regarding external loading conditions and their related failure, as the current study has achieved. Identifying the weak points through experiment and simulation directs manufacturers towards required improvements in particular regions, such as the bottleneck and edge reinforcement during the design and manufacturing phases. Full article

(This article belongs to the Topic Applied System on Biomedical Engineering, Healthcare and Sustainability 2024)

►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Coefficient of friction experimental estimation. (<b>a</b>) CAD representation of friction experiment; (<b>b</b>) Experimental setup.</p> Full article ">Figure 2
<p>Preliminary investigation of ISO recommended tensile test sample shape where a shear failure was noticed rather than anticipated tensile failure; (<b>a</b>) is a front view of the failed sample, and (<b>b</b>) is a back view of the same sample.</p> Full article ">Figure 3
<p>Tensile test setup showing (<b>a</b>) CAD representation of the machine and (<b>b</b>) the single-column Instron uniaxial testing machine with a test specimen.</p> Full article ">Figure 4
<p>Experimental compression test setup with a hot water bottle: (<b>a</b>) the Instron 3369 front view without the setup, (<b>b</b>) the manufactured setup as used in the laboratory, (<b>c</b>) the automatic control unit, (<b>d</b>) 3D isometric projection as designed in Creo Parametric 11 software, and (<b>e</b>) side view showing the test starting position.</p> Full article ">Figure 5
<p>The hot water bottle geometry was created as a CAD file using (<b>a</b>) Creo Parametric 11 software, and its coordinates were represented in (<b>b</b>) MATLAB to facilitate geometry meshing.</p> Full article ">Figure 6
<p>FE model representations of the loading process: (<b>a</b>) initial position, (<b>b</b>) simulating water filling the bottle, (<b>c</b>) simulating upper plate downward displacement, and (<b>d</b>) bottle fully compressed.</p> Full article ">Figure 6 Cont.
<p>FE model representations of the loading process: (<b>a</b>) initial position, (<b>b</b>) simulating water filling the bottle, (<b>c</b>) simulating upper plate downward displacement, and (<b>d</b>) bottle fully compressed.</p> Full article ">Figure 7
<p>Ogden material model fitting results to different orders, (<b>a</b>) first order (N = 1, 83 iterations); (<b>b</b>) second (N = 2, 90 iterations) order; (<b>c</b>) third order (N = 3, 200 iterations), and (<b>d</b>) fourth order (N = 4, 269 iterations). Original data in these subplots refer to the average experimental tensile test results.</p> Full article ">Figure 8
<p>The Yenway EX20 biological microscope while capturing images for the current study.</p> Full article ">Figure 9
<p>Hot water bottle preliminary tensile test sample (<b>a</b>) just before the test, (<b>b</b>) after the test where the expansion was well over the expected operational range.</p> Full article ">Figure 10
<p>Tensile testing results showing (<b>a</b>) load–elongation curves for individual samples and (<b>b</b>) the mean stress–strain curve with standard deviation.</p> Full article ">Figure 11
<p>Compression test results showing (<b>a</b>) load–elongation curves for individual samples and (<b>b</b>) the mean stress–strain curve with standard deviation.</p> Full article ">Figure 12
<p>A set of subfigures showing experimental and simulation outcomes: (<b>a</b>) post-experiment photos show that most samples failed around the neck area during the compression test, (<b>b</b>) FE analysis simulation of the von Mises stress distribution on the bottle, (<b>c</b>) normal stress distribution on the bottle, (<b>d</b>) shear stress distribution on the bottle, (<b>e</b>) FE analysis simulation of the contact pressure distribution on the upper plate, (<b>f</b>) FE analysis simulation of the contact shear distribution in X direction on the upper plate, and (<b>g</b>) FE analysis simulation of the contact shear distribution in Y direction on the upper plate.</p> Full article ">Figure 12 Cont.
<p>A set of subfigures showing experimental and simulation outcomes: (<b>a</b>) post-experiment photos show that most samples failed around the neck area during the compression test, (<b>b</b>) FE analysis simulation of the von Mises stress distribution on the bottle, (<b>c</b>) normal stress distribution on the bottle, (<b>d</b>) shear stress distribution on the bottle, (<b>e</b>) FE analysis simulation of the contact pressure distribution on the upper plate, (<b>f</b>) FE analysis simulation of the contact shear distribution in X direction on the upper plate, and (<b>g</b>) FE analysis simulation of the contact shear distribution in Y direction on the upper plate.</p> Full article ">Figure 13
<p>Comparing the experimentally recorded average of load–contraction with the simulated averaged upper plate reaction force in the vertical direction with the displacement.</p> Full article ">Figure 14
<p>The failed section was imaged with 50× optical microscopy (<b>a</b>) at the central point of critical fracture around the neck region of the hot water bottle, (<b>b</b>) at a point further along the propagated failure line, and (<b>c</b>) at the internal surface at an undamaged point as a control.</p> Full article ">

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-16