Topic Editors

Department of Electronic Engineering, National Formosa University, Yunlin City 632, Taiwan
The Graduate Institute of Science Education and the Department of Earth Sciences, National Taiwan Normal University (NTNU), Taipei, Taiwan
Human and Artificial Cognition Lab, University of Paris 8, Saint-Denis, France
Department of Electrical Engineering, National Central University, Taoyuan 32001, Taiwan
Department of Recreation and Health Care Management, Chia Nan University of Pharmacy & Science, Tainan City 71710, Taiwan

Applied System on Biomedical Engineering, Healthcare and Sustainability 2023

Abstract submission deadline
closed (31 December 2023)
Manuscript submission deadline
31 March 2024
Viewed by
33449

Topic Information

Dear Colleagues,

Recently, healthcare is undergoing a sector-wide transformation thanks 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 well-being-centered (https://www.ecbios.asia/). Healthcare systems, as well as fundamental medicine research, are becoming smarter and utilizing biomedical engineering. Furthermore, with cutting edge sensors and computer technologies, healthcare delivery could also yield better efficiency, higher quality, and lower cost. However, these innovations often do not lead to sustainability, health, and happiness for all people. Science and technology are to be complemented by the arts, humanities, social sciences, and indigenous know-how and wisdom in order to increase the accessibility of the benefits for the needy 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 2023” includes five journals, Journal of Clinical Medicine, Applied SciencesElectronics, Bioengineering, and Healthcare, which aim to publish excellent papers about relative fields. This enables an interdisciplinary collaboration between science and engineering technologists in the academic and industrial fields, as well as internaional networking.

Topics of interest include the following:

  • Smart healthcare system analysis and design;
  • Computer and human–machine interactions in the healthcare system;
  • Application of Internet of Things (IoT) in the 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 and recreation;
  • Health promotion;
  • Nourishment and health care;
  • Disaster and health;
  • Health and the 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 regarding healthcare, sustainability and 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

  • human–machine interactions
  • Internet of Things (IoT)
  • public health
  • biotechnology rehabilitation medicine
  • sports medicine
  • pediatric and geriatric emergency care
  • leisure and recreation
  • health promotion
  • disaster and health
  • health services
  • medical electronics
  • biomedical engineering
  • biomedical engineering
  • sustainability
  • smart healthcare system

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400 Submit
Bioengineering
bioengineering
4.6 4.2 2014 17.7 Days CHF 2700 Submit
Electronics
electronics
2.9 4.7 2012 15.6 Days CHF 2400 Submit
Healthcare
healthcare
2.8 2.7 2013 19.5 Days CHF 2700 Submit
Journal of Clinical Medicine
jcm
3.9 5.4 2012 17.9 Days CHF 2600 Submit
Chips
chips
- - 2022 15.0 days * CHF 1000 Submit

* Median value for all MDPI journals in the second half of 2023.


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Published Papers (28 papers)

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17 pages, 3093 KiB  
Article
Real-Time Compact Digital Processing Chain for the Detection and Sorting of Neural Spikes from Implanted Microelectrode Arrays
Chips 2024, 3(1), 32-48; https://doi.org/10.3390/chips3010002 - 08 Feb 2024
Viewed by 268
Abstract
Implantable microelectrodes arrays are used to record electrical signals from surrounding neurons and have led to incredible improvements in modern neuroscience research. Digital signals resulting from conditioning and the analog-to-digital conversion of neural spikes captured by microelectrodes arrays have to be elaborated in [...] Read more.
Implantable microelectrodes arrays are used to record electrical signals from surrounding neurons and have led to incredible improvements in modern neuroscience research. Digital signals resulting from conditioning and the analog-to-digital conversion of neural spikes captured by microelectrodes arrays have to be elaborated in a dedicated DSP core devoted to a real-time spike-sorting process for the classification phase based on the source neurons from which they were emitted. On-chip spike-sorting is also essential to achieve enough data reduction to allow for wireless transmission within the power constraints imposed on implantable devices. The design of such integrated circuits must meet stringent constraints related to ultra-low power density and the minimum silicon area, as well as several application requirements. The aim of this work is to present real-time hardware architecture able to perform all the spike-sorting tasks on chip while satisfying the aforementioned stringent requirements related to this type of application. The proposed solution has been coded in VHDL language and simulated in the Cadence Xcelium tool to verify the functional behavior of the digital processing chain. Then, a synthesis and place and route flow has been carried out to implement the proposed architecture in both a 130 nm and a FD-SOI 28 nm CMOS process, with a 200 MHz clock frequency target. Post-layout simulations in the Cadence Xcelium tool confirmed the proper operation up to a 200 MHz clock frequency. The area occupation and power consumption of the proposed detection and clustering module are 0.2659 mm2/ch, 7.16 μW/ch, 0.0168 mm2/ch, and 0.47 μW/ch for the 130 nm and 28 nm implementation, respectively. Full article
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15 pages, 2294 KiB  
Article
Sequential Brain CT Image Captioning Based on the Pre-Trained Classifiers and a Language Model
Appl. Sci. 2024, 14(3), 1193; https://doi.org/10.3390/app14031193 - 31 Jan 2024
Viewed by 403
Abstract
Intracerebral hemorrhage (ICH) is a severe cerebrovascular disorder that poses a life-threatening risk, necessitating swift diagnosis and treatment. While CT scans are the most effective diagnostic tool for detecting cerebral hemorrhage, their interpretation typically requires the expertise of skilled professionals. However, in regions [...] Read more.
Intracerebral hemorrhage (ICH) is a severe cerebrovascular disorder that poses a life-threatening risk, necessitating swift diagnosis and treatment. While CT scans are the most effective diagnostic tool for detecting cerebral hemorrhage, their interpretation typically requires the expertise of skilled professionals. However, in regions with a shortage of such experts or situations with time constraints, delays in diagnosis may occur. In this paper, we propose a method that combines a pre-trained CNN classifier and GPT-2 to generate text for sequentially acquired ICH CT images. Initially, CNN undergoes fine-tuning by learning the presence of ICH in publicly available single CT images, and subsequently, it extracts feature vectors (i.e., matrix) from 3D ICH CT images. These vectors are input along with text into GPT-2, which is trained to generate text for consecutive CT images. In experiments, we evaluated the performance of four models to determine the most suitable image captioning model: (1) In the N-gram-based method, ReseNet50V2 and DenseNet121 showed relatively high scores. (2) In the embedding-based method, DenseNet121 exhibited the best performance. (3) Overall, the models showed good performance in BERT score. Our proposed method presents an automatic and valuable approach for analyzing 3D ICH CT images, contributing to the efficiency of ICH diagnosis and treatment. Full article
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21 pages, 3189 KiB  
Article
Area-Power-Delay-Efficient Multi-Modulus Multiplier Based on Area-Saving Hard Multiple Generator Using Radix-8 Booth-Encoding Scheme on Field Programmable Gate Array
Electronics 2024, 13(2), 311; https://doi.org/10.3390/electronics13020311 - 10 Jan 2024
Viewed by 403
Abstract
A multi-modulus architecture based on the radix-8 Booth encoding of a modulo (2n − 1) multiplier, a modulo (2n) multiplier, and a modulo (2n + 1) multiplier is proposed in this paper. It uses the original single circuit and [...] Read more.
A multi-modulus architecture based on the radix-8 Booth encoding of a modulo (2n − 1) multiplier, a modulo (2n) multiplier, and a modulo (2n + 1) multiplier is proposed in this paper. It uses the original single circuit and shares many common circuit characteristics with a small extra circuit to carry out multi-modulus operations. Compared with a previous radix-4 study, the radix-8 architecture can increase the modulation multiplication encoding selection from three codes to four codes. This reduces the use of partial products from ⌊n/2⌋ to ⌊n/3⌋ + 1, but it increases the operation complexity for multiplication by three circuits. A hard multiple generator (HMG) is used to address this problem. Two judgment signals in the multi-modulus circuit can be used to perform three operations of the modulo (2n − 1) multiplier, modulo (2n) multiplier, and modulo (2n + 1) multiplier at the same time. The weighted representation is used to reduce the number of partial products. Compared with previously reported methods in the literature, the proposed approach can achieve better performance by being more area-efficient, being faster, consuming low power, and having a lower area-delay product (ADP) and power-delay product (PDP). With the multi-modulus HMG, the proposed modified architecture can save 34.48–55.23% of hardware area. Compared with previous studies on the multi-modulus multiplier, the proposed architecture can save 22.78–35.46%, 4.12–11.15%, 12.59–24.73%, 27.88–38.88%, and 20.49–27.85% of hardware area, delay time, dissipation power, ADP, and PDP, respectively. Xilinx field programmable gate array (FPGA) Vivado 2019.2 tools and the Verilog hardware description language are used for synthesis and implementation. The Xilinx Artix-7 XC7A35T-CSG324-1 chipset is adopted to evaluate the performance. Full article
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12 pages, 3285 KiB  
Article
Automatic Counting and Location Labeling of Rice Seedlings from Unmanned Aerial Vehicle Images
Electronics 2024, 13(2), 273; https://doi.org/10.3390/electronics13020273 - 08 Jan 2024
Viewed by 566
Abstract
Traditional counting of rice seedlings in agriculture is often labor-intensive, time-consuming, and prone to errors. Therefore, agricultural automation has gradually become a prominent solution. In this paper, UVA detection, combining deep learning with unmanned aerial vehicle (UAV) sensors, contributes to precision agriculture. We [...] Read more.
Traditional counting of rice seedlings in agriculture is often labor-intensive, time-consuming, and prone to errors. Therefore, agricultural automation has gradually become a prominent solution. In this paper, UVA detection, combining deep learning with unmanned aerial vehicle (UAV) sensors, contributes to precision agriculture. We propose a YOLOv4-based approach for the counting and location marking of rice seedlings from unmanned aerial vehicle (UAV) images. The detection of tiny objects is a crucial and challenging task in agricultural imagery. Therefore, we make modifications to the data augmentation and activation functions in the neural elements of the deep learning model to meet the requirements of rice seedling detection and counting. In the preprocessing stage, we segment the UAV images into different sizes for training. Mish activation is employed to enhance the accuracy of the YOLO one-stage detector. We utilize the dataset provided in the AIdea 2021 competition to evaluate the system, achieving an F1-score of 0.91. These results indicate the superiority of the proposed method over the baseline system. Furthermore, the outcomes affirm the potential for precise detection of rice seedlings in precision agriculture. Full article
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12 pages, 1245 KiB  
Study Protocol
Rationale and Design of a Wearable Cardiopulmonary Monitoring System for Improving the Efficiency of Critical Care Monitoring
Appl. Sci. 2023, 13(24), 13101; https://doi.org/10.3390/app132413101 - 08 Dec 2023
Viewed by 3206
Abstract
Despite the recent development of wearable cardiopulmonary monitoring devices and their necessity in clinical settings, the evidence regarding their application in real-world intensive care units (ICUs) is limited. These devices have notable problems, such as inefficient manufacturing and cumbersome hardware for medical staff [...] Read more.
Despite the recent development of wearable cardiopulmonary monitoring devices and their necessity in clinical settings, the evidence regarding their application in real-world intensive care units (ICUs) is limited. These devices have notable problems, such as inefficient manufacturing and cumbersome hardware for medical staff and patients. In this study, we propose a simplified cardiopulmonary monitoring system and present a protocol for a single-center prospective study to evaluate the efficacy of the proposed system compared with those from the conventional monitoring system. The system was designed to continuously measure electrocardiogram, respiration rate, and oxygen saturation in a stand-alone device with an intuitive data visualization platform and automatic data collection. The accuracy of the data measured from the proposed device will be pre-validated by comparing them with those from the reference device. Medical staff from the St. Vincent’s Hospital ICU will complete a five-point Likert-type scale questionnaire regarding their experience with conventional ICU monitoring systems. The result will be compared with the second questionnaire conducted after deploying the system. Since this is a study proposal paper, we do not have any data on this study yet. However, compared with the conventional patient monitoring system, the proposed device should be a promising method to relieve medical staff fatigue and that of the patients who must wear and attach the monitoring device for a long time. Full article
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14 pages, 7789 KiB  
Article
Expression Recognition of Multiple Faces Using a Convolution Neural Network Combining the Haar Cascade Classifier
Appl. Sci. 2023, 13(23), 12737; https://doi.org/10.3390/app132312737 - 28 Nov 2023
Viewed by 781
Abstract
Facial expression serves as the primary means for humans to convey emotions and communicate social signals. In recent years, facial expression recognition has become a viable application within medical systems because of the rapid development of artificial intelligence and computer vision. However, traditional [...] Read more.
Facial expression serves as the primary means for humans to convey emotions and communicate social signals. In recent years, facial expression recognition has become a viable application within medical systems because of the rapid development of artificial intelligence and computer vision. However, traditional facial expression recognition faces several challenges. The approach is designed to investigate the processing of facial expressions in real-time systems involving multiple individuals. These factors impact the accuracy and robustness of the model. In this paper, we adopted the Haar cascade classifier to extract facial features and utilized convolutional neural networks (CNNs) as the backbone model to achieve an efficient system. The proposed approach achieved an accuracy of approximately 70% on the FER-2013 dataset in the experiment. This result represents an improvement of 7.83% compared to that of the baseline system. This significant enhancement improves the accuracy of facial expression recognition. Herein, the proposed approach also extended to multiple face expression recognition; the module was further experimented with and obtained promising results. The outcomes of this research will establish a solid foundation for real-time monitoring and prevention of conditions such as depression through an emotion alert system. Full article
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15 pages, 5272 KiB  
Article
Design, Fabrication, and Preliminary Validation of Patient-Specific Spine Section Phantoms for Use in Training Spine Surgeons Outside the Operating Room/Theatre
Bioengineering 2023, 10(12), 1345; https://doi.org/10.3390/bioengineering10121345 - 23 Nov 2023
Viewed by 692
Abstract
Pedicle screw fixation (PSF) demands rigorous training to mitigate the risk of severe neurovascular complications arising from screw misplacement. This paper introduces a patient-specific phantom designed for PSF training, extending a portion of the learning process beyond the confines of the surgical room. [...] Read more.
Pedicle screw fixation (PSF) demands rigorous training to mitigate the risk of severe neurovascular complications arising from screw misplacement. This paper introduces a patient-specific phantom designed for PSF training, extending a portion of the learning process beyond the confines of the surgical room. Six phantoms of the thoracolumbar region were fabricated from radiological datasets, combining 3D printing and casting techniques. The phantoms were employed in three training sessions by a fifth-year resident who performed full training on all six phantoms; he/she placed a total of 57 pedicle screws. Analysis of the learning curve, focusing on time per screw and positioning accuracy, revealed attainment of an asymptotic performance level (around 3 min per screw) after 40 screws. The phantom’s efficacy was evaluated by three experts and six residents, each inserting a minimum of four screws. Initial assessments confirmed face, content, and construct validity, affirming the patient-specific phantoms as a valuable training resource. These proposed phantoms exhibit great promise as an essential tool in surgical training as they exhibited a demonstrable learning effect on the PSF technique. This study lays the foundation for further exploration and underscores the potential impact of these patient-specific phantoms on the future of spinal surgical education. Full article
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19 pages, 3300 KiB  
Article
In Vivo Assessment of Skin Surface Pattern: Exploring Its Potential as an Indicator of Bone Biomechanical Properties
Bioengineering 2023, 10(12), 1338; https://doi.org/10.3390/bioengineering10121338 - 21 Nov 2023
Viewed by 859
Abstract
The mechanical properties of bone tissue are the result of a complex process involving collagen–crystal interactions. The mineral density of the bone tissue is correlated with bone strength, whereas the characteristics of collagen are often associated with the ductility and toughness of the [...] Read more.
The mechanical properties of bone tissue are the result of a complex process involving collagen–crystal interactions. The mineral density of the bone tissue is correlated with bone strength, whereas the characteristics of collagen are often associated with the ductility and toughness of the bone. From a clinical perspective, bone mineral density alone does not satisfactorily explain skeletal fragility. However, reliable in vivo markers of collagen quality that can be easily used in clinical practice are not available. Hence, the objective of the present study is to examine the relationship between skin surface morphology and changes in the mechanical properties of the bone. An experimental study was conducted on healthy children (n = 11), children with osteogenesis imperfecta (n = 13), and women over 60 years of age (n = 22). For each patient, the skin characteristic length (SCL) of the forearm skin surface was measured. The SCL quantifies the geometric patterns formed by wrinkles on the skin’s surface, both in terms of size and elongation. The greater the SCL, the more deficient was the organic collagen matrix. In addition, the bone volume fraction and mechanical properties of the explanted femoral head were determined for the elderly female group. The mean SCL values of the healthy children group were significantly lower than those of the elderly women and osteogenesis imperfecta groups. For the aged women group, no significant differences were indicated in the elastic mechanical parameters, whereas bone toughness and ductility decreased significantly as the SCL increased. In conclusion, in bone collagen pathology or bone aging, the SCL is significantly impaired. This in vivo skin surface parameter can be a non-invasive tool to improve the estimation of bone matrix quality and to identify subjects at high risk of bone fracture. Full article
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18 pages, 2029 KiB  
Article
The Relationship between Health Insurance and Pharmaceutical Innovation: An Empirical Study Based on Meta-Analysis
Healthcare 2023, 11(22), 2916; https://doi.org/10.3390/healthcare11222916 - 07 Nov 2023
Viewed by 861
Abstract
The growing research interest in the relationship between health insurance and pharmaceutical innovation is driven by their significant impact on healthcare optimization and pharmaceutical development. The existing literature, however, lacks consensus on this relationship and provides no evidence of the magnitude of a [...] Read more.
The growing research interest in the relationship between health insurance and pharmaceutical innovation is driven by their significant impact on healthcare optimization and pharmaceutical development. The existing literature, however, lacks consensus on this relationship and provides no evidence of the magnitude of a correlation. In this context, this study employs meta-analysis to explore the extent to which health insurance affects pharmaceutical innovation. It analyzes 202 observations from 14 independent research samples, using the regression coefficient of health insurance on pharmaceutical innovation as the effect size. The results reveal that there is a strong positive correlation between health insurance and pharmaceutical innovation (r = 0.367, 95% CI = [0.294, 0.436]). Public health insurance exhibits a stronger promoting effect on pharmaceutical innovation than commercial health insurance. The relationship between health insurance and pharmaceutical innovation is moderated by the country of sample origin, data range, journal type, journal impact factor, type of health insurance, and research perspective. Our research findings further elucidate the relationship mechanism between health insurance and pharmaceutical innovation, providing a valuable reference for future explorations in pharmaceutical fields. Full article
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12 pages, 2629 KiB  
Article
Creating an AI-Enhanced Morse Code Translation System Based on Images for People with Severe Disabilities
Bioengineering 2023, 10(11), 1281; https://doi.org/10.3390/bioengineering10111281 - 03 Nov 2023
Viewed by 1058
Abstract
(1) Background: Patients with severe physical impairments (spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis) often have limited mobility due to physical limitations, and may even be bedridden all day long, losing the ability to take care of themselves. In more severe cases, [...] Read more.
(1) Background: Patients with severe physical impairments (spinal cord injury, cerebral palsy, amyotrophic lateral sclerosis) often have limited mobility due to physical limitations, and may even be bedridden all day long, losing the ability to take care of themselves. In more severe cases, the ability to speak may even be lost, making even basic communication very difficult. (2) Methods: This research will design a set of image-assistive communication equipment based on artificial intelligence to solve communication problems of daily needs. Using artificial intelligence for facial positioning, and facial-motion-recognition-generated Morse code, and then translating it into readable characters or commands, it allows users to control computer software by themselves and communicate through wireless networks or a Bluetooth protocol to control environment peripherals. (3) Results: In this study, 23 human-typed data sets were subjected to recognition using fuzzy algorithms. The average recognition rates for expert-generated data and data input by individuals with disabilities were 99.83% and 98.6%, respectively. (4) Conclusions: Through this system, users can express their thoughts and needs through their facial movements, thereby improving their quality of life and having an independent living space. Moreover, the system can be used without touching external switches, greatly improving convenience and safety. Full article
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15 pages, 3612 KiB  
Article
Heart Murmur Classification Using a Capsule Neural Network
Bioengineering 2023, 10(11), 1237; https://doi.org/10.3390/bioengineering10111237 - 24 Oct 2023
Viewed by 1668
Abstract
The healthcare industry has made significant progress in the diagnosis of heart conditions due to the use of intelligent detection systems such as electrocardiograms, cardiac ultrasounds, and abnormal sound diagnostics that use artificial intelligence (AI) technology, such as convolutional neural networks (CNNs). Over [...] Read more.
The healthcare industry has made significant progress in the diagnosis of heart conditions due to the use of intelligent detection systems such as electrocardiograms, cardiac ultrasounds, and abnormal sound diagnostics that use artificial intelligence (AI) technology, such as convolutional neural networks (CNNs). Over the past few decades, methods for automated segmentation and classification of heart sounds have been widely studied. In many cases, both experimental and clinical data require electrocardiography (ECG)-labeled phonocardiograms (PCGs) or several feature extraction techniques from the mel-scale frequency cepstral coefficient (MFCC) spectrum of heart sounds to achieve better identification results with AI methods. Without good feature extraction techniques, the CNN may face challenges in classifying the MFCC spectrum of heart sounds. To overcome these limitations, we propose a capsule neural network (CapsNet), which can utilize iterative dynamic routing methods to obtain good combinations for layers in the translational equivariance of MFCC spectrum features, thereby improving the prediction accuracy of heart murmur classification. The 2016 PhysioNet heart sound database was used for training and validating the prediction performance of CapsNet and other CNNs. Then, we collected our own dataset of clinical auscultation scenarios for fine-tuning hyperparameters and testing results. CapsNet demonstrated its feasibility by achieving validation accuracies of 90.29% and 91.67% on the test dataset. Full article
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11 pages, 1494 KiB  
Article
5G-Based Smart Healthcare and Mobile Network Security: Combating Fake Base Stations
Appl. Sci. 2023, 13(20), 11565; https://doi.org/10.3390/app132011565 - 23 Oct 2023
Viewed by 755
Abstract
New mobile network technologies, particularly 5G, have spurred a growth in smart healthcare networks. They enable real-time monitoring, personalized treatments, and more. However, these transformative capabilities have also uncovered potential vulnerabilities, emphasizing the urgency to safeguard patient data and healthcare services. This study [...] Read more.
New mobile network technologies, particularly 5G, have spurred a growth in smart healthcare networks. They enable real-time monitoring, personalized treatments, and more. However, these transformative capabilities have also uncovered potential vulnerabilities, emphasizing the urgency to safeguard patient data and healthcare services. This study analyzes the existing research on 5G-based smart healthcare network security with a specific emphasis on fake base station attacks. The research investigates potential security measures to mitigate the impact of fake base station attacks. And based on those findings, we propose a detection scheme to help combat fake base station threats effectively and to avoid the need to install individual apps on smart devices, providing a foothold for future efforts to develop and deploy better countermeasures. To ensure a secure and resilient ecosystem for 5G-based smart healthcare, continuous research and proactive measures are required. By staying vigilant and committed to research and development, we can protect patient privacy, ensure secure data transmission, and enhance the quality of services within smart healthcare networks and other mobile network applications alike. Full article
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12 pages, 1846 KiB  
Article
UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images
Appl. Sci. 2023, 13(19), 10800; https://doi.org/10.3390/app131910800 - 28 Sep 2023
Cited by 1 | Viewed by 696
Abstract
The human digestive system is susceptible to various viruses and bacteria, which can lead to the development of lesions, disorders, and even cancer. According to statistics, colorectal cancer has been a leading cause of death in Taiwan for years. To reduce its mortality [...] Read more.
The human digestive system is susceptible to various viruses and bacteria, which can lead to the development of lesions, disorders, and even cancer. According to statistics, colorectal cancer has been a leading cause of death in Taiwan for years. To reduce its mortality rate, clinicians must detect and remove polyps during gastrointestinal (GI) tract examinations. Recently, colonoscopies have been conducted to examine patients’ colons. Even so, polyps sometimes remain undetected. To help medical professionals better identify abnormalities, advanced deep learning algorithms that can accurately detect colorectal polyps from images should be developed. Prompted by this proposition, the present study combined U-Net and YOLOv4 to create a two-stage network algorithm called UY-Net. This new algorithm was tested using colonoscopy images from the Kvasir-SEG dataset. Results showed that UY-Net was significantly accurate in detecting polyps. It also outperformed YOLOv4, YOLOv3-spp, Faster R-CNN, and RetinaNet by achieving higher spatial accuracy and overall accuracy of object detection. As the empirical evidence suggests, two-stage network algorithms like UY-Net will be a reliable and promising aid to image detection in healthcare. Full article
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13 pages, 1993 KiB  
Article
Influence of Induced Environment Oscillations on Limits of Stability in Healthy Adults
Appl. Sci. 2023, 13(18), 10331; https://doi.org/10.3390/app131810331 - 15 Sep 2023
Viewed by 418
Abstract
(1) Background: Human balance and equilibrium-maintaining abilities have been widely researched up to this day. Numerous publications have investigated the possibilities of enhancing these abilities, bringing the patient back to their original capabilities post-disease or accident, and training for fall prevention. Virtual reality [...] Read more.
(1) Background: Human balance and equilibrium-maintaining abilities have been widely researched up to this day. Numerous publications have investigated the possibilities of enhancing these abilities, bringing the patient back to their original capabilities post-disease or accident, and training for fall prevention. Virtual reality technology (VR) is becoming a progressively more renowned technique for performing or enhancing rehabilitation or training. We aimed to explore whether the introduction of scenery oscillation can influence a person’s limits of stability. (2) Methods: Sixteen healthy adults participated in measurements. Each of them underwent 10 trials, during which subjects were supposed to, on acoustic cue, lean as far forward and back as possible, without raising their heels or toes. Two trials were conducted without the use of VR, four with oscillating scenery, one with stationary scenery, one with displayed darkness, and two trials were performed for reference, which did not require leaning nor used VR technology. (3) Results: For the total as well as for each foot separately, COP displacements and velocities were calculated and analyzed. A post-hoc Wilcoxon pairwise test with Holm’s correction was performed, resulting in 420 returned p-values, 4 of which indicated significant differences between medians when comparing trials with 0.2 Hz oscillating scenery with trials with eyes open and closed. (4) Conclusions: No statistically significant differences at α = 0.05 between reached maximums in trials using VR and trials without it were found, only trials using 0.2 Hz oscillations displayed statistically significant differences when comparing velocities of leaning. The authors believe that such oscillations resemble naturally occurring tinnitus; additionally, low-frequency oscillations are believed to influence postural balance more than high-frequency ones, therefore affecting the velocity and displacements of COP the most. Full article
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15 pages, 235 KiB  
Article
Exploring Internal Conflicts and Collaboration of a Hospital Home Healthcare Team: A Grounded Theory Approach
Healthcare 2023, 11(18), 2478; https://doi.org/10.3390/healthcare11182478 - 07 Sep 2023
Viewed by 670
Abstract
An aging society is on the rise, leading to a variety of caregiving issues. The Taiwanese government has been implementing a home healthcare integration plan since 2015, aimed at integrating and forming interdisciplinary care teams with medical institutions. This study explores the internal [...] Read more.
An aging society is on the rise, leading to a variety of caregiving issues. The Taiwanese government has been implementing a home healthcare integration plan since 2015, aimed at integrating and forming interdisciplinary care teams with medical institutions. This study explores the internal conflict factors among hospital home healthcare team members at a district teaching hospital in Taichung, Taiwan, and it seeks a better collaboration model between them. Semi-structured in-depth interviews were conducted with seven hospital home healthcare team members. Data analysis was based on grounded theory, with research quality relying on the triangulation and consistency analysis methods. The results show that “work overload”, “resource overuse”, “inconsistent assessment”, “limited resources”, “communication cost”, and “lack of incentives” are the major conflicts among the team. This study proposed the following collaboration model, including “identifying the internal stakeholders of a home healthcare team” and “the key stakeholders as referral coordinators”, “patient-centered resource allocation”, and “teamwork orientation”. The study recommends that within a teamwork-oriented home healthcare team, its members should proactively demonstrate their role responsibilities and actively provide support to one another. Only through patient-centered resource allocation and mutual respect can the goal of seamless home healthcare be achieved. The content of the research and samples were approved by the hospital ethics committee (REC108-18). Full article
16 pages, 7515 KiB  
Article
A Novel Elastic Sensor Sheet for Pressure Injury Monitoring: Design, Integration, and Performance Analysis
Electronics 2023, 12(17), 3655; https://doi.org/10.3390/electronics12173655 - 30 Aug 2023
Viewed by 756
Abstract
This study presents the SENSOMATT sensor sheet, a novel, non-invasive pressure monitoring technology intended for placement beneath a mattress. The development and design process of the sheet, which includes a novel sensor arrangement, material selection, and incorporation of an elastic rubber sheet, is [...] Read more.
This study presents the SENSOMATT sensor sheet, a novel, non-invasive pressure monitoring technology intended for placement beneath a mattress. The development and design process of the sheet, which includes a novel sensor arrangement, material selection, and incorporation of an elastic rubber sheet, is investigated in depth. Highlighted features include the ability to adjust to varied mattress sizes and the incorporation of AI technology for pressure mapping. A comparison with conventional piezoelectric contact sensor sheets demonstrates the better accuracy of the SENSOMATT sensor for monitoring pressures beneath a mattress. The report highlights the sensor network’s cost-effectiveness, durability, and enhanced data measurement, alongside the problems experienced in its design. Evaluations of performance under diverse settings contribute to a full understanding of its potential pressure injury prediction and patient care applications. Proposed future paths for the SENSOMATT sensor sheet include clinical validation, more cost and performance improvement, wireless connection possibilities, and improved long-term monitoring data analysis. The study concludes that the SENSOMATT sensor sheet has the potential to transform pressure injury prevention techniques in healthcare. Full article
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13 pages, 2254 KiB  
Article
Convolutional Neural Network and Language Model-Based Sequential CT Image Captioning for Intracerebral Hemorrhage
Appl. Sci. 2023, 13(17), 9665; https://doi.org/10.3390/app13179665 - 26 Aug 2023
Cited by 1 | Viewed by 660
Abstract
Intracerebral hemorrhage is a severe problem where more than one-third of patients die within a month. In diagnosing intracranial hemorrhage, neuroimaging examinations are essential. As a result, the interpretation of neuroimaging becomes a crucial process in medical procedures. However, human-based image interpretation has [...] Read more.
Intracerebral hemorrhage is a severe problem where more than one-third of patients die within a month. In diagnosing intracranial hemorrhage, neuroimaging examinations are essential. As a result, the interpretation of neuroimaging becomes a crucial process in medical procedures. However, human-based image interpretation has inherent limitations, as it can only handle a restricted range of tasks. To address this, a study on medical image captioning has been conducted, but it primarily focused on single medical images. However, actual medical images often consist of continuous sequences, such as CT scans, making it challenging to directly apply existing studies. Therefore, this paper proposes a CT image captioning model that utilizes a 3D-CNN model and distilGPT-2. In this study, four combinations of 3D-CNN models and language models were compared and analyzed for their performance. Additionally, the impact of applying penalties to the loss function and adjusting penalty values during the training process was examined. The proposed CT image captioning model demonstrated a maximum BLEU score of 0.35 on the in-house dataset, and it was observed that the text generated by the model became more similar to human interpretations in medical image reports with the application of loss function penalties. Full article
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14 pages, 1701 KiB  
Article
Improved Lightweight Convolutional Neural Network for Finger Vein Recognition System
Bioengineering 2023, 10(8), 919; https://doi.org/10.3390/bioengineering10080919 - 03 Aug 2023
Cited by 1 | Viewed by 1066
Abstract
Computer vision (CV) technology and convolutional neural networks (CNNs) demonstrate superior feature extraction capabilities in the field of bioengineering. However, during the capturing process of finger-vein images, translation can cause a decline in the accuracy rate of the model, making it challenging to [...] Read more.
Computer vision (CV) technology and convolutional neural networks (CNNs) demonstrate superior feature extraction capabilities in the field of bioengineering. However, during the capturing process of finger-vein images, translation can cause a decline in the accuracy rate of the model, making it challenging to apply CNNs to real-time and highly accurate finger-vein recognition in various real-world environments. Moreover, despite CNNs’ high accuracy, CNNs require many parameters, and existing research has confirmed their lack of shift-invariant features. Based on these considerations, this study introduces an improved lightweight convolutional neural network (ILCNN) for finger vein recognition. The proposed model incorporates a diverse branch block (DBB), adaptive polyphase sampling (APS), and coordinate attention mechanism (CoAM) with the aim of improving the model’s performance in accurately identifying finger vein features. To evaluate the effectiveness of the model in finger vein recognition, we employed the finger-vein by university sains malaysia (FV-USM) and PLUSVein dorsal-palmar finger-vein (PLUSVein-FV3) public database for analysis and comparative evaluation with recent research methodologies. The experimental results indicate that the finger vein recognition model proposed in this study achieves an impressive recognition accuracy rate of 99.82% and 95.90% on the FV-USM and PLUSVein-FV3 public databases, respectively, while utilizing just 1.23 million parameters. Moreover, compared to the finger vein recognition approaches proposed in previous studies, the ILCNN introduced in this work demonstrated superior performance. Full article
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12 pages, 7177 KiB  
Article
An Experimental Evaluation of Respiration by Monitoring Ribcage Motion
Appl. Sci. 2023, 13(15), 8938; https://doi.org/10.3390/app13158938 - 03 Aug 2023
Viewed by 647
Abstract
This paper aims to make an early diagnosis of respiratory disorders by measuring ribcage motion. A statistically significant numerical evaluation of the biomechanics of respiration is obtained through the acquisition of the kinematics of the sixth rib. We report the results of an [...] Read more.
This paper aims to make an early diagnosis of respiratory disorders by measuring ribcage motion. A statistically significant numerical evaluation of the biomechanics of respiration is obtained through the acquisition of the kinematics of the sixth rib. We report the results of an experimental campaign that has been conducted using a RESPIRholter prototype for efficient and comfortable respiration monitoring on two groups of volunteers, one with healthy people and the other with chest-operated patients. The data from repeated acquisitions are statistically processed to analyze results in terms of angular motion and linear acceleration, which can be used to characterize and classify respiration motion. This experimental campaign can be considered a first result for the construction of a database useful for a reference of diagnostics, as reported by the discussed example case study. Full article
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16 pages, 3793 KiB  
Article
An Embedded System Based on Raspberry Pi for Effective Electrocardiogram Monitoring
Appl. Sci. 2023, 13(14), 8273; https://doi.org/10.3390/app13148273 - 17 Jul 2023
Cited by 3 | Viewed by 1937
Abstract
In recent years, there has been a growing demand for affordable and user-friendly medical diagnostic devices due to the rise in global diseases. This study focuses on the development of an embedded system based on Raspberry Pi that enables faster and more efficient [...] Read more.
In recent years, there has been a growing demand for affordable and user-friendly medical diagnostic devices due to the rise in global diseases. This study focuses on the development of an embedded system based on Raspberry Pi that enables faster and more efficient monitoring of electrocardiogram (ECG). The incorporation of Raspberry Pi allows for both wireless and wired interfaces, facilitating the creation of an ECG diagnostic embedded system capable of real-time detection and immediate response to any abnormalities in heart functionality. The system presented in this research encompasses a comprehensive electronic circuit comprising analog and digital components to measure and display the ECG signal. Within the analog section, the circuit performs essential signal conditioning tasks, such as signal amplification and noise filtering, ensuring a clean signal within the desired frequency range. The entire system is powered using a power bank. The digital segment incorporates an analog-to-digital converter necessary for converting the received analog signal into a digital format compatible with Raspberry Pi. A graphical liquid-crystal display is utilized to display the measured signal. The device successfully measures ECG signals at various heart rates, capturing all crucial peaks that can be used as indicators of an individual’s health condition. By comparing the signals obtained from healthy individuals with those exhibiting heart arrhythmias, valuable insights can be gained regarding their health status. The proposed system aims to be portable, cost-effective, and user-friendly in different environments. Full article
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15 pages, 332 KiB  
Article
Factors Influencing the Effectiveness of E-Learning in Healthcare: A Fuzzy ANP Study
Healthcare 2023, 11(14), 2035; https://doi.org/10.3390/healthcare11142035 - 16 Jul 2023
Cited by 1 | Viewed by 1931
Abstract
E-learning has transformed the healthcare education system by providing healthcare professionals with training and development opportunities, regardless of their location. However, healthcare professionals in remote or rural areas face challenges such as limited access to educational resources, lack of reliable internet connectivity, geographical [...] Read more.
E-learning has transformed the healthcare education system by providing healthcare professionals with training and development opportunities, regardless of their location. However, healthcare professionals in remote or rural areas face challenges such as limited access to educational resources, lack of reliable internet connectivity, geographical isolation, and limited availability of specialized training programs and instructors. These challenges hinder their access to e-learning opportunities and impede their professional development. To address this issue, a study was conducted to identify the factors that influence the effectiveness of e-learning in healthcare. A literature review was conducted, and two questionnaires were distributed to e-learning experts to assess primary variables and identify the most significant factor. The Fuzzy Analytic Network Process (Fuzzy ANP) was used to identify the importance of selected factors. The study found that success, satisfaction, availability, effectiveness, readability, and engagement are the main components ranked in order of importance. Success was identified as the most significant factor. The study results highlight the benefits of e-learning in healthcare, including increased accessibility, interactivity, flexibility, knowledge management, and cost efficiency. E-learning offers a solution to the challenges of professional development faced by healthcare professionals in remote or rural areas. The study provides insights into the factors that influence the effectiveness of e-learning in healthcare and can guide the development of future e-learning programs. Full article
17 pages, 5513 KiB  
Article
An Electronic Microsaccade Circuit with Charge-Balanced Stimulation and Flicker Vision Prevention for an Artificial Eyeball System
Electronics 2023, 12(13), 2836; https://doi.org/10.3390/electronics12132836 - 27 Jun 2023
Viewed by 849
Abstract
This paper presents the first circuit that enables microsaccade function in an artificial eyeball system. Currently, the artificial eyeball is receiving increasing development in vision restoration. The main challenge is that the human eye is born with microsaccade that helps refresh vision, avoiding [...] Read more.
This paper presents the first circuit that enables microsaccade function in an artificial eyeball system. Currently, the artificial eyeball is receiving increasing development in vision restoration. The main challenge is that the human eye is born with microsaccade that helps refresh vision, avoiding perception fading while the gaze is fixed for a long period, and without microsaccade, high-quality vision restoration is difficult. The proposed electronic microsaccade (E-μSaccade) circuit addresses the issue, and it is intrinsically safe because only charge-balanced stimulus pulses are allowed for stimulation. The E-μSaccade circuit adopts light-to-frequency modulation; due to the circuit’s leakage and dark current of light-sensitive elements, stimulus pulses of a frequency lower than tens of Hz occur, which is the cause of flickering vision. A flicker vision prevention (FVP) circuit is proposed to mitigate the issue. The proposed circuits are designed in a 0.18 μm standard CMOS process. The simulation and measurement results show that the E-μSaccade circuit helps refresh the stimulation pattern and blocks the low-frequency output. Full article
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12 pages, 2309 KiB  
Communication
Electromagnetic Imaging for Buried Conductors Using Deep Convolutional Neural Networks
Appl. Sci. 2023, 13(11), 6794; https://doi.org/10.3390/app13116794 - 02 Jun 2023
Cited by 1 | Viewed by 754
Abstract
In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we [...] Read more.
In the past, many conventional algorithms, such as self-adaptive dynamic differential evolution and asynchronous particle swarm optimization, were used to reconstruct buried objects in the frequency domain; these were unfortunately time-consuming during the iterative, repeated computing process of the scattered field. Consequently, we propose an innovative deep convolutional neural network approach to solve the electromagnetic inverse scattering problem for buried conductors in this paper. Different shapes of conductors are buried in one half-space and the electromagnetic wave from the other half-space is incident. The shape of the conductor can be reconstructed promptly by inputting the received scattered fields measured from the upper half-space into the deep convolutional neural network module, which avoids the computational complexity of Green’s function for training. Numerical results show that the root mean square error for differently shaped—circular, elliptical, arrow, peanut, four-petal, and three-petal—reconstructed images are, respectively, 2.95%, 3.11%, 17.81%, 15.10%, 14.14%, and 15.24%. Briefly speaking, not only can circular and elliptical buried conductors be reconstructed; some irregular shapes can be reconstructed well. On the contrary, the reconstruction result by U-Net for buried objects is worse since it is not able to obtain a good preliminary image by processing only the upper scattered field—that is, rather than the full space. In other words, our proposed deep convolutional neural network can efficiently solve the electromagnetic inverse scattering problem of buried conductors and provide a novel method for the microwave imaging of the buried conductors. This is the first successful attempt at using deep convolutional neural networks for buried conductors in the frequency domain, which may be useful for practical applications in various fields such as the medical, military, or industrial fields, including magnetic resonance imaging, mine detection and clearance, non-destructive testing, gas or wire pipeline detection, etc. Full article
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13 pages, 688 KiB  
Article
Microwave Imaging for Half-Space Conductors Using the Whale Optimization Algorithm and the Spotted Hyena Optimizer
Appl. Sci. 2023, 13(10), 5857; https://doi.org/10.3390/app13105857 - 09 May 2023
Viewed by 820
Abstract
This research implements the whale optimization algorithm (WOA) and spotted hyena optimizer (SHO) in inverse scattering to regenerate the conductor shape concealed in the half-space. TM waves are irradiated from the other half-space to a perfect conductor with an unknown shape buried in [...] Read more.
This research implements the whale optimization algorithm (WOA) and spotted hyena optimizer (SHO) in inverse scattering to regenerate the conductor shape concealed in the half-space. TM waves are irradiated from the other half-space to a perfect conductor with an unknown shape buried in one half-space. The scattered field measured outside the conductor surface with the boundary condition is used to reconstruct the object using the WOA and SHO algorithms. Several scenarios of reconstruction accuracy were compared for the WOA and SHO. The numerical simulations prove that the WOA has a better reconstruction capability. Full article
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16 pages, 7799 KiB  
Article
Leakage Current Detector and Warning System Integrated with Electric Meter
Electronics 2023, 12(9), 2123; https://doi.org/10.3390/electronics12092123 - 06 May 2023
Viewed by 3641
Abstract
Electrical power is essential in human life. Thus, the security and reliability of its supply are of critical importance in a country’s industrial and economic development. The leakage and improper use of electricity may cause serious problems such as fire and electrocution. To [...] Read more.
Electrical power is essential in human life. Thus, the security and reliability of its supply are of critical importance in a country’s industrial and economic development. The leakage and improper use of electricity may cause serious problems such as fire and electrocution. To prevent such incidents and minimize the loss of life and property, a leakage current detector and warning system are developed in this study. With a high-precision current transformer and high-gain linear converter, the detector effectively detects leakage current over 1 mA, which is validated in different methods. The detector can be integrated into widely used electric meters (Taipower Datong’s sub-meter model D4S) easily, and information on detected leakage current is transmitted to the cloud server through narrowband IoT wireless communication (NB-IoT) to warn users and management personnel of the electrical power line. The proposed detector and system are expected to prevent the fire caused by leakage current which was the main cause of the fire at homes and buildings and can be an effective means to manage the electrical powerline system and metering facilities. Full article
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15 pages, 4460 KiB  
Article
Using Contactless Facial Image Recognition Technology to Detect Blood Oxygen Saturation
Bioengineering 2023, 10(5), 524; https://doi.org/10.3390/bioengineering10050524 - 26 Apr 2023
Cited by 1 | Viewed by 1948
Abstract
Since the outbreak of COVID-19, as of January 2023, there have been over 670 million cases and more than 6.8 million deaths worldwide. Infections can cause inflammation in the lungs and decrease blood oxygen levels, which can lead to breathing difficulties and endanger [...] Read more.
Since the outbreak of COVID-19, as of January 2023, there have been over 670 million cases and more than 6.8 million deaths worldwide. Infections can cause inflammation in the lungs and decrease blood oxygen levels, which can lead to breathing difficulties and endanger life. As the situation continues to escalate, non-contact machines are used to assist patients at home to monitor their blood oxygen levels without encountering others. This paper uses a general network camera to capture the forehead area of a person’s face, using the RPPG (remote photoplethysmography) principle. Then, image signal processing of red and blue light waves is carried out. By utilizing the principle of light reflection, the standard deviation and mean are calculated, and the blood oxygen saturation is computed. Finally, the effect of illuminance on the experimental values is discussed. The experimental results of this paper were compared with a blood oxygen meter certified by the Ministry of Health and Welfare in Taiwan, and the experimental results had only a maximum error of 2%, which is better than the 3% to 5% error rates in other studies The measurement time was only 30 s, which is better than the one minute reported using similar equipment in other studies. Therefore, this paper not only saves equipment expenses but also provides convenience and safety for those who need to monitor their blood oxygen levels at home. Future applications can combine the SpO2 detection software with camera-equipped devices such as smartphones and laptops. The public can detect SpO2 on their own mobile devices, providing a convenient and effective tool for personal health management. Full article
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15 pages, 2842 KiB  
Article
Towards a Rapid-Turnaround Low-Depth Unbiased Metagenomics Sequencing Workflow on the Illumina Platforms
Bioengineering 2023, 10(5), 520; https://doi.org/10.3390/bioengineering10050520 - 25 Apr 2023
Cited by 1 | Viewed by 1409
Abstract
Unbiased metagenomic sequencing is conceptually well-suited for first-line diagnosis as all known and unknown infectious entities can be detected, but costs, turnaround time and human background reads in complex biofluids, such as plasma, hinder widespread deployment. Separate preparations of DNA and RNA also [...] Read more.
Unbiased metagenomic sequencing is conceptually well-suited for first-line diagnosis as all known and unknown infectious entities can be detected, but costs, turnaround time and human background reads in complex biofluids, such as plasma, hinder widespread deployment. Separate preparations of DNA and RNA also increases costs. In this study, we developed a rapid unbiased metagenomics next-generation sequencing (mNGS) workflow with a human background depletion method (HostEL) and a combined DNA/RNA library preparation kit (AmpRE) to address this issue. We enriched and detected bacterial and fungal standards spiked in plasma at physiological levels with low-depth sequencing (<1 million reads) for analytical validation. Clinical validation also showed 93% of plasma samples agreed with the clinical diagnostic test results when the diagnostic qPCR had a Ct < 33. The effect of different sequencing times was evaluated with the 19 h iSeq 100 paired end run, a more clinically palatable simulated iSeq 100 truncated run and the rapid 7 h MiniSeq platform. Our results demonstrate the ability to detect both DNA and RNA pathogens with low-depth sequencing and that iSeq 100 and MiniSeq platforms are compatible with unbiased low-depth metagenomics identification with the HostEL and AmpRE workflow. Full article
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18 pages, 3408 KiB  
Article
A Non-Invasive Optical Multimodal Photoplethysmography-Near Infrared Spectroscopy Sensor for Measuring Intracranial Pressure and Cerebral Oxygenation in Traumatic Brain Injury
Appl. Sci. 2023, 13(8), 5211; https://doi.org/10.3390/app13085211 - 21 Apr 2023
Cited by 2 | Viewed by 1440
Abstract
(1) Background: Traumatic brain injuries (TBI) result in high fatality and lifelong disability rates. Two of the primary biomarkers in assessing TBI are intracranial pressure (ICP) and brain oxygenation. Both are assessed using standalone techniques, out of which ICP can only be assessed [...] Read more.
(1) Background: Traumatic brain injuries (TBI) result in high fatality and lifelong disability rates. Two of the primary biomarkers in assessing TBI are intracranial pressure (ICP) and brain oxygenation. Both are assessed using standalone techniques, out of which ICP can only be assessed utilizing invasive techniques. The motivation of this research is the development of a non-invasive optical multimodal monitoring technology for ICP and brain oxygenation which will enable the effective management of TBI patients. (2) Methods: a multiwavelength optical sensor was designed and manufactured so as to assess both parameters based on the pulsatile and non-pulsatile signals detected from cerebral backscatter light. The probe consists of four LEDs and three photodetectors that measure photoplethysmography (PPG) and near-infrared spectroscopy (NIRS) signals from cerebral tissue. (3) Results: The instrumentation system designed to acquire these optical signals is described in detail along with a rigorous technical evaluation of both the sensor and instrumentation. Bench testing demonstrated the right performance of the electronic circuits while a signal quality assessment showed good indices across all wavelengths, with the signals from the distal photodetector being of highest quality. The system performed well within specifications and recorded good-quality pulsations from a head phantom and provided non-pulsatile signals as expected. (4) Conclusions: This development paves the way for a multimodal non-invasive tool for the effective assessment of TBI patients. Full article
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