/pdf/flexible-and-wearable-biosensors-for-monitoring-health-14p0j6uc.pdf

Flexible and Wearable Biosensors for Monitoring Health Conditions

Extracellular vesicles (EVs) are membrane structures enclosed by a lipid bilayer that are released into the extracellular space by all types of cells. EVs are involved in many physiological processes by transporting biologically active substances. Interest in EVs for diagnostic biomarker research and therapeutic drug delivery applications has increased in recent years. The realization of the full therapeutic potential of EVs is currently hampered by the lack of a suitable technology for the isolation and purification of EVs for downstream pharmaceutical applications. Anion Exchange Chromatography (AEX) is an established method in which specific charges on the AEX matrix can exploit charges on the surface of EVs and their interactions to provide a productive and scalable separation and purification method. The established AEX method using Eshmuno® Q, a strong tentacle anion exchange resin, was used to demonstrate the principal feasibility of AEX-based isolation and gain insight into isolated EV properties. Using several EV analysis techniques to provide a more detailed insight into EV populations during AEX isolation, we demonstrated that although the composition of CD9/63/81 remained constant for tetraspanin positive EVs, the size distribution and purity changed during elution. Higher salt concentrations eluted larger tetraspanin negative vesicles.

Novel insights into the isolation of extracellular vesicles by anion exchange chromatography

Stretchable sensors have been widely investigated and developed for the purpose of human motion detection, touch sensors, and healthcare monitoring, typically converting mechanical/structural deformation into electrical signals. The viscoelastic strain of stretchable materials often results in nonlinear stress-strain characteristics over a broad range of strains, consequently making the stretchable sensors at the body joints less accurate in predicting and recognizing human gestures. Accurate recognition of human gestures can be further deteriorated by environmental changes such as temperature and humidity. Here, we demonstrated an environment-adaptable high stress-strain linearity (up to ε = 150%) and high-durability (>100,000 cycles) stretchable sensor conformally laminated onto the body joints for human gesture recognition. The serpentine configuration of our ionic liquid-based stretchable film enabled us to construct broad data sets of mechanical strain and temperature changes for machine learning-based gesture recognition. Signal recognition and training of distinct strains and environmental stimuli using a machine learning-based algorithm analysis successfully measured and predicted the joint motion in a temperature-changing environment with an accuracy of 92.86% (R-squared). Therefore, we believe that our serpentine-shaped ion gel-based stretchable sensor harmonized with machine-learning analysis will be a significant achievement toward environmentally adaptive and multianalyte sensing applications. Our proposed machine learning-enabled multisensor system may enable the development of future electronic devices such as wearable electronics, soft robotics, electronic skin, and human-machine interaction systems.

Machine Learning-Enabled Environmentally Adaptable Skin-Electronic Sensor for Human Gesture Recognition.

Self-powered wearable Internet of Things (IoT) sensors have made a significant impact on human life and health in recent years. These sensors are known for their convenience, durability, affordability, and longevity, leading to substantial improvements in people’s lives. This review summarizes the development of self-powered wearable IoT sensors in recent years. Materials for self-powered wearable sensors are summarized and evaluated, including nanomaterials, flexible materials, and degradable materials. The working mode of self-powered wearable IoT sensors is analyzed, and the different principles of its physical sensing and chemical sensing are explained. Several common technologies for self-powered wearable IoT sensors are presented, such as triboelectric technology, piezoelectric technology, and machine learning. The applications of self-powered IoT wearable sensors in human-machine interfaces are reviewed. Its current shortcomings and prospects for its future development are also discussed. To conduct this review, a comprehensive literature search was performed using several electronic databases, resulting in the inclusion of 225 articles. The gathered data was extracted, synthesized, and analyzed using a thematic analysis approach. This review provides a comprehensive analysis and summary of its working mode, technologies, and applications and provides references and inspiration for related research in this field. Furthermore, this review also identifies the key directions and challenges for future research. 

Self-powered wearable IoT sensors as human-machine interfaces

Artificial intelligence (AI) techniques offer great potential to advance point-of-care testing (POCT) and wearable sensors for personalized medicine applications. This review explores the recent advances and the transformative potential of the use of AI in improving wearables and POCT. The integration of AI significantly contributes to empowering these tools and enables continuous monitoring, real-time analysis, and rapid diagnostics, thus enhancing patient outcomes and healthcare efficiency. Wearable sensors powered by AI models offer tremendous opportunities for precise and non-invasive tracking of physiological conditions that are essential for early disease detection and personalized treatments. AI-empowered POCT facilitates rapid, accurate diagnostics, making these medical testing kits accessible and available even in resource-limited settings. This review discusses the key advances in AI applications for data processing, sensor fusion, and multivariate analytics, highlighting case examples that exhibit their impact in different medical scenarios. In addition, the challenges associated with data privacy, regulatory approvals, and technology integrations into the existing healthcare system have been overviewed. The outlook emphasizes the urgent need for continued innovation in AI-driven health technologies to overcome these challenges and to fully achieve the potential of these techniques to revolutionize personalized medicine. 

AI-Reinforced Wearable Sensors and Intelligent Point-of-Care Tests

/pdf/self-powered-biosensors-for-monitoring-human-physiological-2zu8sbjs.pdf

Self-Powered Biosensors for Monitoring Human Physiological Changes

As an emerging training method, blood flow restriction training has been proved to promote the growth of muscle mass and strength. In recent years, it has been gradually applied in different populations. However, there are few studies on how blood flow restriction training affects muscle mass and strength in the elderly. The relevant literature is compiled and summarized in this study. Through the comparison of blood flow restriction training with traditional training methods and its application in the elderly, it shows that blood flow restriction training can effectively increase muscle mass and strength, prevent muscle atrophy, improve cardiopulmonary function, facilitate injury and postoperative rehabilitation, and intervene in related degenerative diseases as a training method suitable for the elderly,. The main mechanism of blood flow restriction training promoting muscle mass and strength growth is metabolic stress response, including muscle fiber recruitment, protein synthesis signal pathway activation, hormone secretion, etc., and is also related to cell swelling caused by pressure. At present, although the application of blood flow restriction training in the elderly population is increasing, there is a lack of personalized programs. In the future, more research on the dose effect and safety of blood flow restriction training is needed to develop more accurate personalized training programs.

Application and progress of blood flow restriction training in improving muscle mass and strength in the elderly

Since the discovery of ion-exchange resins, they have been used in many fields, including pharmacy. Ion-exchange resin-mediated preparations can realize a series of functions, such as taste masking and regulating release. However, it is very difficult to extract the drug completely from the drug–resin complex because of the specific combination of the drug and resin. In this study, methylphenidate hydrochloride extended-release chewable tablets compounded by methylphenidate hydrochloride and ion-exchange resin were selected for a drug extraction study. The efficiency of drug extraction by dissociating with the addition of counterions was found to be higher than other physical extraction methods. Then, the factors affecting the dissociation process were studied to completely extract the drug from the methylphenidate hydrochloride extended-release chewable tablets. Furthermore, the thermodynamic and kinetic study of the dissociation process showed that the dissociation process obeys the second-order kinetic process, and it is nonspontaneous, entropy-decreasing, and endothermic. Meanwhile, the reaction rate was confirmed by the Boyd model, and the film diffusion and matrix diffusion were both shown to be rate-limiting steps. In conclusion, this study aims to provide technological and theoretical support for establishing a quality assessment and control system of ion-exchange resin-mediated preparations, promoting the applications of ion-exchange resins in the field of drug preparation.

/pdf/methods-and-characteristics-of-drug-extraction-from-ion-3bbvawhr.pdf

Methods and Characteristics of Drug Extraction from Ion-Exchange-Resin-Mediated Preparations: Influences, Thermodynamics, and Kinetics

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Self-Powered Biosensors for Monitoring Human Physiological Changes

Application and progress of blood flow restriction training in improving muscle mass and strength in the elderly

Methods and Characteristics of Drug Extraction from Ion-Exchange-Resin-Mediated Preparations: Influences, Thermodynamics, and Kinetics