The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Engineered two-dimensional nanomaterials based diagnostics integrated with internet of medical things (IoMT) for COVID-19

More than four years have passed since an inimitable coronavirus disease (COVID-19) pandemic hit the globe in 2019 after an uncontrolled transmission of the severe acute respiratory syndrome (SARS-CoV-2) infection. The occurrence of this highly contagious respiratory infectious disease led to chaos and mortality all over the world. The peak paradigm shift of the researchers was inclined towards the accurate and rapid detection of diseases. Since 2019, there has been a boost in the diagnostics of COVID-19 via numerous conventional diagnostic tools like RT-PCR, ELISA, etc., and advanced biosensing kits like LFIA, etc. For the same reason, the use of nanotechnology and two-dimensional nanomaterials (2DNMs) has aided in the fabrication of efficient diagnostic tools to combat COVID-19. This article discusses the engineering techniques utilized for fabricating chemically active E2DNMs that are exceptionally thin and irregular. The techniques encompass the introduction of heteroatoms, intercalation of ions, and the design of strain and defects. E2DNMs possess unique characteristics, including a substantial surface area and controllable electrical, optical, and bioactive properties. These characteristics enable the development of sophisticated diagnostic platforms for real-time biosensors with exceptional sensitivity in detecting SARS-CoV-2. Integrating the Internet of Medical Things (IoMT) with these E2DNMs-based advanced diagnostics has led to the development of portable, real-time, scalable, more accurate, and cost-effective SARS-CoV-2 diagnostic platforms. These diagnostic platforms have the potential to revolutionize SARS-CoV-2 diagnosis by making it faster, easier, and more accessible to people worldwide, thus making them ideal for resource-limited settings. These advanced IoMT diagnostic platforms may help with combating SARS-CoV-2 as well as tracking and predicting the spread of future pandemics, ultimately saving lives and mitigating their impact on global health systems.

One-Pot Synthesis of a Robust Crosslinker-Free Thermo-Reversible Conducting Hydrogel Electrode for Epidermal Electronics

Traditional epidermal electrodes, typically made of silver/silver chloride (Ag/AgCl), have been widely used in various applications, including electrophysiological recordings and biosignal monitoring. However, they present limitations due to inherent material mismatches with the skin. This often results in high interface impedance, discomfort, and potential skin irritation, particularly during prolonged use or for individuals with sensitive skin. While various tissue-mimicking materials have been explored, their mechanical advantages often come at the expense of conductivity, resulting in low-quality recordings. We herein report the facile fabrication of conducting and stretchable hydrogels using a "one-pot" method. This approach involves the synthesis of a natural hydrogel, termed Golde, composed of abundant and eco-friendly components, including gelatin, chitosan, and glycerol. To enhance the conductivity of the hydrogel, various conducting materials, such as poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT:PSS), thermally reduced graphene (TRG), and MXene, are introduced. The resulting conducting hydrogels exhibit remarkable robustness, do not require crosslinkers, and possess a unique thermo-reversible property, simplifying the fabrication process and ensuring enhanced long-term stability. Moreover, their fabrication is sustainable, as it employs environmentally friendly materials and processes while retaining their skin-friendly characteristics. The resulting hydrogel electrodes were tested for electrocardiogram (ECG) signal acquisition and outperformed commercial electrodes even when implemented in an all-flexible electrode setup simply using copper tape, owing to their inherent adhesiveness.

Conducting polymer scaffolds: a new frontier in bioelectronics and bioengineering

Conducting polymer (CP) scaffolds have emerged as a transformative tool in bioelectronics and bioengineering, advancing the ability to interface with biological systems. Their unique combination of electrical conductivity, tailorability, and biocompatibility surpasses the capabilities of traditional nonconducting scaffolds while granting them access to the realm of bioelectronics. This review examines recent developments in CP scaffolds, focusing on material and device advancements, as well as their interplay with biological systems. We highlight applications for monitoring, tissue stimulation, and drug delivery and discuss perspectives and challenges currently faced for their ultimate translation and clinical implementation.

Rational Design of Flexible Mechanical Force Sensors for Healthcare and Diagnosis

Over the past decade, there has been a significant surge in interest in flexible mechanical force sensing devices and systems. Tremendous efforts have been devoted to the development of flexible mechanical force sensors for daily healthcare and medical diagnosis, driven by the increasing demand for wearable/portable devices in long-term healthcare and precision medicine. In this review, we summarize recent advances in diverse categories of flexible mechanical force sensors, covering piezoresistive, capacitive, piezoelectric, triboelectric, magnetoelastic, and other force sensors. This review focuses on their working principles, design strategies and applications in healthcare and diagnosis, with an emphasis on the interplay among the sensor architecture, performance, and application scenario. Finally, we provide perspectives on the remaining challenges and opportunities in this field, with particular discussions on problem-driven force sensor designs, as well as developments of novel sensor architectures and intelligent mechanical force sensing systems.

Body landmarks and genetic algorithm-based approach for non-contact detection of head forward posture among Chinese adolescents: revitalizing machine learning in medicine

Addressing the current complexities, costs, and adherence issues in the detection of forward head posture (FHP), our study conducted an exhaustive epidemiologic investigation, incorporating a comprehensive posture screening process for each participant in China. This research introduces an avant-garde, machine learning-based non-contact method for the accurate discernment of FHP. Our approach elevates detection accuracy by leveraging body landmarks identified from human images, followed by the application of a genetic algorithm for precise feature identification and posture estimation. Observational data corroborates the superior efficacy of the Extra Tree Classifier technique in FHP detection, attaining an accuracy of 82.4%, a specificity of 85.5%, and a positive predictive value of 90.2%. Our model affords a rapid, effective solution for FHP identification, spotlighting the transformative potential of the convergence of feature point recognition and genetic algorithms in non-contact posture detection. The expansive potential and paramount importance of these applications in this niche field are therefore underscored.