Challenges of Emerging Wearable Sensors for Remote Monitoring toward Telemedicine Healthcare

A smartphone-based immunochromatographic strip platform for on-site quantitative detection of antigenic targets

To report the testing signal of an immunochromatographic assay for on-site quantitative detection, a portable and user-friendly smartphone-based biosensing platform is developed in this study. This innovative system is composed of an ambient light sensor inherent smartphone reader and a 3D-printed handhold device, a quantitative tool capable of directly interpreting carbon nanoparticle (CNP)-conjugated immunochromatographic strips. To showcase the platform capability, the smartphone-based immunochromatography system (SPICS) reader and device were successfully used in CNP strips for rapid detection of the early pregnancy marker human chorionic gonadotropin in female urine (HCG; limit of detection [LOD]: 0.30 mIU mL-1), prostate-specific antigen in patient blood (PSA; LOD: 0.28 ng mL-1) and ampicillin residue in animal milk (AMP; LOD: 0.23 ng mL-1). The results were fully correlated with conventional commercial instruments (R2 = 0.99). The SPICS platform exhibits significant advantages, including portability, cost-effectiveness, easy operation, and rapid and quantitative detection, making it a valuable on-site diagnosis tool for use in home and community healthcare facilities.

Artificial intelligence-aiding lab-on-a-chip workforce designed oral [3.1.0] bi and [4.2.0] tricyclic catalytic interceptors inhibiting multiple SARS-CoV-2 protomers assisted by double-shell deep learning

While each massive pandemic has claimed the lives of millions of vulnerable populations over the centuries, one limitation exists: that the Edisonian approach (human-directed with trial errors) relies on repurposing pharmaceuticals, designing drugs, and herbal remedies with the violation of Lipinski's rule of five druglikeness. It may lead to adverse health effects with long-term health multimorbidity. Nevertheless, declining birth rates and aging populations will likely cause a shift in society due to a shortage of a scientific workforce to defend against the next pandemic incursion. The challenge of combating the ongoing post-COVID-19 pandemic has been exacerbated by the lack of gold standard drugs to deactivate multiple SARS-CoV-2 protein targets. Meanwhile, there are three FDA-approved antivirals, Remdesivir, Molnupiravir, and Paxlovid, with moderate clinical efficacy and drug resistance. There is a pressing need for additional antivirals and prepared omics technology to combat the current and future devastating coronavirus pandemics. While there is a limitation of existing contemporary inhibitors to deactivate viral RNA replication with minimal rotational bonds, one strategy is to create Lipinski inhibitors with less than 10 rotational bonds and precise halogen bond placement to destabilize multiple viral protomers. This work describes the efforts to design gold-standard oral inhibitors of bi- and tri-cyclic catalytic interceptors with electrophilic heads using double-shell deep learning. Here, KS1 with and KS2 compounds designed by lab-on-a-chip technology attain 5-fold novel filtered-Lipinski, GHOSE, VEBER, EGAN, and MUEGGE druglikeness. The graph neural network (GNN) relies on module-initiation, expansion, relabeling atom index, and termination (METORITE) iterations, while the deep neural network (DNN) engages pinning, extraction, convolution, pooling, and flattening (PROOF) operations. The cyclic compound's specific halogen atom location enhances the nitrile catalytic head, which deactivates several viral protein targets. Initiating this lab-on-a-chip that is not susceptible to the aging process for creating clinical compounds can leverage a new path to many valuable drugs with speedy oral drug discovery, especially to defend the loss of vulnerable population and prevent multimorbidity that is susceptible to hidden viral persistence in the continuing aging times.

Innovations in Biosensor Technologies for Healthcare Diagnostics and Therapeutic Drug Monitoring: Applications, Recent Progress, and Future Research Challenges

This comprehensive review delves into the forefront of biosensor technologies and their critical roles in disease biomarker detection and therapeutic drug monitoring. It provides an in-depth analysis of various biosensor types and applications, including enzymatic sensors, immunosensors, and DNA sensors, elucidating their mechanisms and specific healthcare applications. The review highlights recent innovations such as integrating nanotechnology, developing wearable devices, and trends in miniaturisation, showcasing their transformative potential in healthcare. In addition, it addresses significant sensitivity, specificity, reproducibility, and data security challenges, proposing strategic solutions to overcome these obstacles. It is envisaged that it will inform strategic decision-making, drive technological innovation, and enhance global healthcare outcomes by synthesising multidisciplinary insights.

Non-Invasive Sensors Integration for NCDs with AIoT Based Telemedicine System

Thailand's hospitals face overcrowding, particularly with non-communicable disease (NCD) patients, due to a doctor shortage and an aging population. Most literature showed implementation merely on web or mobile application to teleconsult with physicians. Instead, in this work, we developed and implemented a telemedicine health kiosk system embedded with non-invasive biosensors and time-series predictors to improve NCD indicators over an eight-month period. Two cohorts were randomly selected: a control group with usual care and a telemedicine-using group. The telemedicine-using group showed significant improvements in average fasting blood glucose (148 to 130 mg/dL) and systolic blood pressure (152 to 138 mmHg). Data mining with the Apriori algorithm revealed correlations between diseases, occupations, and environmental factors, informing public health policies. Communication between kiosks and servers used LoRa, 5G, and IEEE802.11, which are selected based on the distance and signal availability. The results support telemedicine kiosks as effective for NCD management, significantly improving key NCD indicators, average blood glucose, and blood pressure.

Photonic Solutions for Challenges in Sensing

Sensing technologies support timely and critical decisions to save precious resources in healthcare, veterinary care, food safety, and environmental protection. However, the design of sensors demands strict technical characteristics for real-world applications. In this Viewpoint, we discuss the main challenges to tackle in the sensing field and how photonics represents a valuable tool in this sphere.

How Plasmon Ag Nanoparticles can Enhance the Power Performance of a Thermoelectric Generator

The development of wearable thermoelectric generators (wTEG) represents a promising strategy to replace batteries and supercapacitors required to supply electrical energy for portable electronic devices. However, the main drawback of wTEGs is that the thermal gradient between the skin and the ambient is minimal, reducing the power output produced by the generator. Therefore, it is necessary to improve the thermal management of wTEG in order to increase its efficiency. This work deals with the preparation of a thermoelectric generator that harnesses the plasmonic heating effect to enhance the thermal gradient of the final device. The thermoelectric layer is created through the in situ polymerization of terthiophene (3T) within a polyurethane matrix, utilizing silver (Ag) (I) and copper (II) perchlorate as oxidants. The plasmonic film, composed of Ag-NP (nanoparticles), is formed via photocatalytic reduction of silver nitrate in the presence of titanium oxide. These layers are then meticulously assembled to yield the hybrid plasmonic/thermoelectric generator.

Current On-Skin Flexible Sensors, Materials, Manufacturing Approaches, and Study Trends for Health Monitoring: A Review

Due to an ever-increasing amount of the population focusing more on their personal health, thanks to rising living standards, there is a pressing need to improve personal healthcare devices. These devices presently require laborious, time-consuming, and convoluted procedures that heavily rely on cumbersome equipment, causing discomfort and pain for the patients during invasive methods such as sample-gathering, blood sampling, and other traditional benchtop techniques. The solution lies in the development of new flexible sensors with temperature, humidity, strain, pressure, and sweat detection and monitoring capabilities, mimicking some of the sensory capabilities of the skin. In this review, a comprehensive presentation of the themes regarding flexible sensors, chosen materials, manufacturing processes, and trends was made. It was concluded that carbon-based composite materials, along with graphene and its derivates, have garnered significant interest due to their electromechanical stability, extraordinary electrical conductivity, high specific surface area, variety, and relatively low cost.

Compensation Method for Missing and Misidentified Skeletons in Nursing Care Action Assessment by Improving Spatial Temporal Graph Convolutional Networks

With the increasing aging population, nursing care providers have been facing a substantial risk of work-related musculoskeletal disorders (WMSDs). Visual-based pose estimation methods, like OpenPose, are commonly used for ergonomic posture risk assessment. However, these methods face difficulty when identifying overlapping and interactive nursing tasks, resulting in missing and misidentified skeletons. To address this, we propose a skeleton compensation method using improved spatial temporal graph convolutional networks (ST-GCN), which integrates kinematic chain and action features to assess skeleton integrity and compensate for it. The results verified the effectiveness of our approach in optimizing skeletal loss and misidentification in nursing care tasks, leading to improved accuracy in calculating both skeleton joint angles and REBA scores. Moreover, comparative analysis against other skeleton compensation methods demonstrated the superior performance of our approach, achieving an 87.34% REBA accuracy score. Collectively, our method might hold promising potential for optimizing the skeleton loss and misidentification in nursing care tasks.

Fully Integrated Point-of-Care Platform for the Self-Monitoring of Phenylalanine in Finger-Prick Blood

Development of point-of-care platforms combining reliability and ease of use is a challenge for the evolution of sensing in healthcare technologies. Here, we report the development and testing of a fully integrated enzymatic colorimetric assay for the sensing of phenylalanine in blood samples from phenylketonuria patients. The platform works with a customized mobile app for data acquisition and visualization and comprises an electronic system and a disposable sensor. The sensing approach is based on specific enzymatic phenylalanine recognition, and the optical transduction method is based on in situ gold nanostructure formation. The phenylketonuria (PKU) smart sensor platform is conceived to perform self-monitoring on phenylalanine levels and real-time therapy tuning, thanks to the direct connection with clinicians. Validation of the technologies with a population of patients affected by PKU, together with the concurrent validation of the platform through centralized laboratories, has confirmed the good analytical performances in terms of sensitivity and specificity, robustness, and utility for phenylalanine sensing. The self-monitoring of phenylalanine for the daily identification of abnormal health conditions could facilitate rapid therapy tuning, improving the wellness of PKU patients.

PCL/PEO Polymer Membrane Prevents Biofouling in Wearable Detection Sensors

Technological advances in biosensing offer extraordinary opportunities to transfer technologies from a laboratory setting to clinical point-of-care applications. Recent developments in the field have focused on electrochemical and optical biosensing platforms. Unfortunately, these platforms offer relatively poor sensitivity for most of the clinically relevant targets that can be measured on the skin. In addition, the non-specific adsorption of biomolecules (biofouling) has proven to be a limiting factor compromising the longevity and performance of these detection systems. Research from our laboratory seeks to capitalize on analyte selective properties of biomaterials to achieve enhanced analyte adsorption, enrichment, and detection. Our goal is to develop a functional membrane integrated into a microfluidic sampling interface and an electrochemical sensing unit. The membrane was manufactured from a blend of Polycaprolactone (PCL) and Polyethylene oxide (PEO) through a solvent casting evaporation method. A microfluidic flow cell was developed with a micropore array that allows liquid to exit from all pores simultaneously, thereby imitating human perspiration. The electrochemical sensing unit consisted of planar gold electrodes for the monitoring of nonspecific adsorption of proteins utilizing Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS). The solvent casting evaporation technique proved to be an effective method to produce membranes with the desired physical properties (surface properties and wettability profile) and a highly porous and interconnected structure. Permeability data from the membrane sandwiched in the flow cell showed excellent permeation and media transfer efficiency with uniform pore activation for both active and passive sweat rates. Biofouling experiments exhibited a decrease in the extent of biofouling of electrodes protected with the PCL/PEO membrane, corroborating the capacity of our material to mitigate the effects of biofouling.

Self-Repairing and Energy-Harvesting Triboelectric Sensor for Tracking Limb Motion and Identifying Breathing Patterns

The increasing prevalence of health problems stemming from sedentary lifestyles and evolving workplace cultures has placed a substantial burden on healthcare systems. Consequently, remote health wearable monitoring systems have emerged as essential tools to track individuals' health and well-being. Self-powered triboelectric nanogenerators (TENGs) have exhibited significant potential for use as emerging detection devices capable of recognizing body movements and monitoring breathing patterns. However, several challenges remain to be addressed in order to fulfill the requirements for self-healing ability, air permeability, energy harvesting, and suitable sensing materials. These materials must possess high flexibility, be lightweight, and have excellent triboelectric charging effects in both electropositive and electronegative layers. In this work, we investigated self-healable electrospun polybutadiene-based urethane (PBU) as a positive triboelectric layer and titanium carbide (Ti3C2Tx) MXene as a negative triboelectric layer for the fabrication of an energy-harvesting TENG device. PBU consists of maleimide and furfuryl components as well as hydrogen bonds that trigger the Diels-Alder reaction, contributing to its self-healing properties. Moreover, this urethane incorporates a multitude of carbonyl and amine groups, which create dipole moments in both the stiff and the flexible segments of the polymer. This characteristic positively influences the triboelectric qualities of PBU by facilitating electron transfer between contacting materials, ultimately resulting in high output performance. We employed this device for sensing applications to monitor human motion and breathing pattern recognition. The soft and fibrous-structured TENG generates a high and stable open-circuit voltage of up to 30 V and a short-circuit current of 4 μA at an operation frequency of 4.0 Hz, demonstrating remarkable cyclic stability. A significant feature of our TENG is its self-healing ability, which allows for the restoration of its functionality and performance after sustaining damage. This characteristic has been achieved through the utilization of the self-healable PBU fibers, which can be repaired via a simple vapor solvent method. This innovative approach enables the TENG device to maintain optimal performance and continue functioning effectively even after multiple uses. After integration with a rectifier, the TENG can charge various capacitors and power 120 LEDs. Moreover, we employed the TENG as a self-powered active motion sensor, attaching it to the human body to monitor various body movements for energy-harvesting and sensing purposes. Additionally, the device demonstrates the capability to recognize breathing patterns in real time, offering valuable insights into an individual's respiratory health.