Smart Detecting and Versatile Wearable Electrical Sensing Mediums for Healthcare

Wearable biosensors for pediatric hospitals: a scoping review

As wearable biosensors are increasingly used in healthcare settings, this review aimed to identify the types of wearable biosensors used for neonate and pediatric patients and how these biosensors were clinically evaluated. A literature search was conducted using PubMed, CINAHL, Embase, Web of Science, and Cochrane. The studies published between January 2010 and February 2024 were included. Descriptive statistics were used to present counts and percentages of types, locations, clinical evaluation methods, and their results. Seventy-nine studies were included. 104 wearable sensors and 40 devices were identified. The most common type of biosensor was optoelectrical sensors (n = 40, 38.5%), and used to measure heart rate (n = 22, 19.0%). The clinical evaluation was tested by a combination of validity (n = 68, 86.1%) and reliability (n = 14, 17.7%). Only two-thirds of the wearable devices were validated or reported acceptable reliability. The majority of the biosensor studies (n = 51, 64.5%) did not report any complications related to wearable biosensors. The current literature has gaps regarding clinical evaluation and safety of wearable biosensor devices with interchangeable use of validity and reliability terms. There is a lack of comprehensive reporting on complications, highlighting the need for standardized guidelines in the clinical evaluation of biosensor medical devices. IMPACT: The most common types of biosensors in pediatric settings were optoelectrical sensors and electrical sensors. Only two-thirds of the wearable devices were validated or reported acceptable reliability, and more than half of the biosensor studies did not report whether they assessed any complications related to wearable biosensors. This review discovered significant gaps in safety and clinical validation reporting, emphasizing the need for standardized guidelines. The findings advocate for improved reporting clinical validation processes to enhance the safety of wearable biosensors in pediatric care.

A Highly Stretchable, Conductive, and Transparent Bioadhesive Hydrogel as a Flexible Sensor for Enhanced Real-Time Human Health Monitoring

Real-time continuous monitoring of non-cognitive markers is crucial for the early detection and management of chronic conditions. Current diagnostic methods are often invasive and not suitable for at-home monitoring. An elastic, adhesive, and biodegradable hydrogel-based wearable sensor with superior accuracy and durability for monitoring real-time human health is developed. Employing a supramolecular engineering strategy, a pseudo-slide-ring hydrogel is synthesized by combining polyacrylamide (pAAm), β-cyclodextrin (β-CD), and poly 2-(acryloyloxy)ethyltrimethylammonium chloride (AETAc) bio ionic liquid (Bio-IL). This novel approach decouples conflicting mechano-chemical effects arising from different molecular building blocks and provides a balance of mechanical toughness (1.1 × 106 Jm-3), flexibility, conductivity (≈0.29 S m-1), and tissue adhesion (≈27 kPa), along with rapid self-healing and remarkable stretchability (≈3000%). Unlike traditional hydrogels, the one-pot synthesis avoids chemical crosslinkers and metallic nanofillers, reducing cytotoxicity. While the pAAm provides mechanical strength, the formation of the pseudo-slide-ring structure ensures high stretchability and flexibility. Combining pAAm with β-CD and pAETAc enhances biocompatibility and biodegradability, as confirmed by in vitro and in vivo studies. The hydrogel also offers transparency, passive-cooling, ultraviolet (UV)-shielding, and 3D printability, enhancing its practicality for everyday use. The engineered sensor demonstratesimproved efficiency, stability, and sensitivity in motion/haptic sensing, advancing real-time human healthcare monitoring.

Optimized multi-frequency nonlinear broadband piezoelectric energy harvester designs

Many electrical devices can be powered and operated by harvesting the wasted energy of the surroundings. This research aims to overcome the challenges of output power with a sharp peak, small bandwidth, and the huge dimensions of the piezoelectric energy harvesters relative to the output power. The aforementioned challenges motivated us to investigate the effect of nonlinearity in the shape (tapered and straight cross-section area) as well as the fixation method (the number of fastened ends) to determine the optimal design with high output power and wide working frequency. This research proposes a novel piezoelectric energy harvester array, where each beam is made up of three fixed beams that are joined together by a center mass. The proposed design produces an output power of 35 mW between 25 and 40 Hz. The output power of the proposed design is 3.24 times more than the conventional designs. The recommended approach is simulated utilizing finite element analysis FEA. Analytical and experimental methods validate the proposed FEA, which exhibits excellent agreement.

Simple and cost-effective pH and T sensors from top to bottom: New chemical probes based on sonogel-carbon transducers for plasma analyses

The present work introduces two novel approaches to fabricate simple and cost-effective pH and temperature probes. Sinusoidal voltage methodologies were employed to electrodeposit polyaniline (PANI) at different growth times (10-20 min) on the surface of an affordable Sonogel-Carbon electrode to conform a robust pH sensor. The presence of PANI and its phases were corroborated by electrochemical means. The sensibility, reversibility and selectivity of the produced sensor were very adequate to apply it in physiological samples. In this regard, the proposed sensor was evaluated in artificial blood serum as well as untreated plasma samples obtaining outstanding results in comparison with a gold reference technique (error <2 %). In addition, a new composite sonogel material, intrinsically modified with multiwalled carbon nanotubes, was attached on top of an electrode couple to one-step fabricate a new temperature probe, relating resistance of the probe with the surroundings temperature. In this case, an optical microscopy characterization was performed to study the sturdiness of the layer. Remarkably, suitable results in terms of sensitivity and selectivity were obtained. The probes were assessed in artificial and untreated plasma samples as well, with the corresponding validation step (error <1 %) by using a commercial temperature probe.

Clinical Trials and Therapeutic Approaches for Healthcare Challenges in Pakistan

Pakistan faces tremendous challenges in providing healthcare due to a lack of consistent policymaking, increasing expenditure and exponential growth in population since its inception in 1947. These challenges are not just driven by politics, policy and allocation of resources but also by healthcare, environment and characteristics of the population biology. Clinical trials provide the best way to find population-specific, cost-effective treatments that do not merely mimic those used in wealthier nations. This article analyzes all clinical studies conducted with at least one site in Pakistan listed on ClinicalTrials.gov, combined with a short overview that considers new therapeutic approaches that can be investigated in future clinical trials. Therapies using repurposed medicines are of particular interest as they use affordable drugs that are already widely available.

Long-Short-Term-Memory-Based Deep Stacked Sequence-to-Sequence Autoencoder for Health Prediction of Industrial Workers in Closed Environments Based on Wearable Devices

To reduce the risks and challenges faced by frontline workers in confined workspaces, accurate real-time health monitoring of their vital signs is essential for improving safety and productivity and preventing accidents. Machine-learning-based data-driven methods have shown promise in extracting valuable information from complex monitoring data. However, practical industrial settings still struggle with the data collection difficulties and low prediction accuracy of machine learning models due to the complex work environment. To tackle these challenges, a novel approach called a long short-term memory (LSTM)-based deep stacked sequence-to-sequence autoencoder is proposed for predicting the health status of workers in confined spaces. The first step involves implementing a wireless data acquisition system using edge-cloud platforms. Smart wearable devices are used to collect data from multiple sources, like temperature, heart rate, and pressure. These comprehensive data provide insights into the workers' health status within the closed space of a manufacturing factory. Next, a hybrid model combining deep learning and support vector machine (SVM) is constructed for anomaly detection. The LSTM-based deep stacked sequence-to-sequence autoencoder is specifically designed to learn deep discriminative features from the time-series data by reconstructing the input data and thus generating fused deep features. These features are then fed into a one-class SVM, enabling accurate recognition of workers' health status. The effectiveness and superiority of the proposed approach are demonstrated through comparisons with other existing approaches.