Self-powered freestanding multifunctional microneedle-based extended gate device for personalized health monitoring

A multi-channel wearable sensing patch based on gate-all-around field-effect transistors

Field-effect transistors (FETs), known for their rapid response and signal amplification capabilities, have attracted significant research interest for the detection of biomarkers. However, the development of multi-channel sensors using FETs and their wearable applications are impeded by the rigid substrates and large areas. Here, we reported a wearable EGFET sensor array patch that integrates gate-all-around field-effect transistors (GAA FETs) and flexible printed circuit board (FPCB) patches to overcome these challenges. The patch takes advantage of the excellent electrical properties and small size of GAA FETs, allowing for multi-biomarker detection. Additionally, it integrates large-scale, low-cost FPCB-based electrodes to enhance the flexibility of the patch. Comprehensive characterization experiments have demonstrated the performance of the patch to detect multiple biomarkers, including glucose, lactate, Na+, K+, and Ca2+. This innovative patch is promising to facilitate the development of FET-based multi-channel wearable sensors, and has the potential to help realize more comprehensive health monitoring.

Applications of Carbon-Based Multivariable Chemical Sensors for Analyte Recognition

Over recent decades, carbon-based chemical sensor technologies have advanced significantly. Nevertheless, significant opportunities persist for enhancing analyte recognition capabilities, particularly in complex environments. Conventional monovariable sensors exhibit inherent limitations, such as susceptibility to interference from coexisting analytes, which results in response overlap. Although sensor arrays, through modification of multiple sensing materials, offer a potential solution for analyte recognition, their practical applications are constrained by intricate material modification processes. In this context, multivariable chemical sensors have emerged as a promising alternative, enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition, while utilizing a single sensing material. Among various carbon-based materials, carbon nanotubes (CNTs) and graphene have emerged as ideal candidates for constructing high-performance chemical sensors, owing to their well-established batch fabrication processes, superior electrical properties, and outstanding sensing capabilities. This review examines the progress of carbon-based multivariable chemical sensors, focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition. The discussion encompasses fundamental aspects of these sensors, including sensing materials, sensor architectures, performance metrics, pattern recognition algorithms, and multivariable sensing mechanism. Furthermore, the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.

Revolutionizing healthcare: A review on cutting-edge innovations in Raspberry Pi-powered health monitoring sensors

The integration of Raspberry Pi technology into health care is a significant advancement that has the potential to revolutionise the delivery of healthcare. This study highlights the inventive uses of Raspberry Pi devices, emphasizing their economical nature, mobility, and capacity to be customised for unique healthcare requirements. Healthcare practitioners may utilize the computational capabilities of Raspberry Pi to create portable monitoring devices that can gather, analyze, and send patient data in real-time. An important benefit of Raspberry Pi-based systems is their capability to facilitate remote patient monitoring, which allows for early diagnosis of diseases and personalized healthcare interventions. This capacity shows potential for people in situations with low resources, when typical monitoring methods may not be available or feasible. Moreover, the capacity of Raspberry Pi technology to easily adjust and be used by a wide range of people makes it a powerful tool for tackling many healthcare concerns. The article promotes the need for ongoing study and advancement in health monitoring systems that utilize Raspberry Pi technology. It emphasizes the need of collaboration among technology enthusiasts, healthcare practitioners, and researchers. By cultivating these collaborations, progress in healthcare solutions based on Raspberry Pi may be expedited, resulting in enhanced patient results and more efficient healthcare provision. The Raspberry Pi technology has the capacity to bring about significant changes in healthcare by effectively meeting the changing needs of current healthcare systems. Healthcare practitioners may optimize patient care, facilitate early intervention, and ultimately boost global health outcomes by utilizing the capabilities of Raspberry Pi devices.

Integration Strategies and Formats in Field-Effect Transistor Chemo- and Biosensors: A Critical Review

The continuous advances in micro- and nanofabrication technologies have inevitably led to major improvements in field-effect transistor (FET) design and architecture, significantly reducing the component footprint and enabling highly efficient integration into many electronic devices. Combined efforts in the areas of materials science, life sciences, and electronic engineering have unlocked opportunities to create ultrasensitive FET chemo- and biosensor devices that are coupled with more diverse and complex integration requirements in terms of hardware interfacing, reproducible functionality, and handling of analyte samples. Integration of FET chemo- and biosensors remains one of the major bottlenecks in bridging the gap between fundamental research concepts and commercial sensing devices. In this review, we critically discuss different strategies and formats of integration in the context of key requirements, fabrication scalability, and device complexity. The intentions of this review are 1) to provide a practical overview of successful FET sensor integration approaches, 2) to identify crucial challenges and factors limiting the extent of FET sensor integration, and 3) to highlight promising perspectives for future developments of FET sensor integration. We believe that our structured insights will be helpful for scientists and engineers of various profiles focusing on the design and development of FET-based chemo- and biosensor devices.

Smart Wearable Sensor Fuels Noninvasive Body Fluid Analysis

The advancements in wearable sensor technology have revolutionized noninvasive body fluid monitoring, offering new possibilities for continuous and real-time health assessment. By analyzing body fluids such as sweat, saliva, tears, and interstitial fluid, these technologies provide painless diagnostic alternatives for detecting biomarkers such as glucose, electrolytes, and metabolites. These sensors play a crucial role in early disease detection, chronic condition management, and personalized healthcare. Recent innovations in flexible electronics, microfluidic systems, and biosensing materials have significantly improved the accuracy, reliability, and integration of sensors into everyday textiles. Moreover, the convergence of artificial intelligence and big data analytics has enhanced the precision and personalization of health monitoring systems, transforming wearable sensors into powerful tools for health holographic inspection. Despite significant progress, challenges remain, including improving sensor stability in dynamic environments, achieving real-time data transmission, and covering a broader range of biomarkers. Future research directions focus on enhancing material sustainability through green synthesis, optimizing sampling techniques, and leveraging machine learning to further improve sensor performance. This Review highlights the transformative potential of wearable sensors in medical applications, aiming to bridge gaps in healthcare accessibility and elevate the standards of patient care through noninvasive continuous monitoring technologies.

Advances in integrated power supplies for self-powered bioelectronic devices

Bioelectronic devices with medical functions have attracted widespread attention in recent years. Power supplies are crucial components in these devices, which ensure their stable operation. Biomedical devices that utilize external power supplies and extended electrical wires limit patient mobility and increase the risk of discomfort and infection. To address these issues, self-powered devices with integrated power supplies have emerged, including triboelectric nanogenerators, piezoelectric nanogenerators, thermoelectric generators, batteries, biofuel cells, solar cells, wireless power transfer, and hybrid energy systems. This mini-review highlights the recent advances in the power supplies utilized in these self-powered devices. A concluding section discusses the subsisting challenges and future perspectives in integrated power supply technologies and design and manufacturing of self-powered devices.

Microneedle sensors for dermal interstitial fluid analysis

The rapid advancement in personalized healthcare has driven the development of wearable biomedical devices for real-time biomarker monitoring and diagnosis. Traditional invasive blood-based diagnostics are painful and limited to sporadic health snapshots. To address these limitations, microneedle-based sensing platforms have emerged, utilizing interstitial fluid (ISF) as an alternative biofluid for continuous health monitoring in a minimally invasive and painless manner. This review aims to provide a comprehensive overview of microneedle sensor technology, covering microneedle design, fabrication methods, and sensing strategy. Additionally, it explores the integration of monitoring electronics for continuous on-body monitoring. Representative applications of microneedle sensing platforms for both monitoring and therapeutic purposes are introduced, highlighting their potential to revolutionize personalized healthcare. Finally, the review discusses the remaining challenges and future prospects of microneedle technology.

Graphical Abstract

Polymeric Microneedles for Health Care Monitoring: An Emerging Trend

Bioanalyte collection by blood draw is a painful process, prone to needle phobia and injuries. Microneedles can be engineered to penetrate the epidermal skin barrier and collect analytes from the interstitial fluid, arising as a safe, painless, and effective alternative to hypodermic needles. Although there are plenty of reviews on the various types of microneedles and their use as drug delivery systems, there is a lack of systematization on the application of polymeric microneedles for diagnosis. In this review, we focus on the current state of the art of this field, while providing information on safety, preclinical and clinical trials, and market distribution, to outline what we believe will be the future of health monitoring.

Real-time prognostic biomarkers for predicting in-hospital mortality and cardiac complications in COVID-19 patients

Hospitalized patients with Coronavirus disease 2019 (COVID-19) are highly susceptible to in-hospital mortality and cardiac complications such as atrial arrhythmias (AA). However, the utilization of biomarkers such as potassium, B-type natriuretic peptide, albumin, and others for diagnosis or the prediction of in-hospital mortality and cardiac complications has not been well established. The study aims to investigate whether biomarkers can be utilized to predict mortality and cardiac complications among hospitalized COVID-19 patients. Data were collected from 6,927 hospitalized COVID-19 patients from March 1, 2020, to March 31, 2021 at one quaternary (Henry Ford Health) and five community hospital registries (Trinity Health Systems). A multivariable logistic regression prediction model was derived using a random sample of 70% for derivation and 30% for validation. Serum values, demographic variables, and comorbidities were used as input predictors. The primary outcome was in-hospital mortality, and the secondary outcome was onset of AA. The associations between predictor variables and outcomes are presented as odds ratio (OR) with 95% confidence intervals (CIs). Discrimination was assessed using area under ROC curve (AUC). Calibration was assessed using Brier score. The model predicted in-hospital mortality with an AUC of 90% [95% CI: 88%, 92%]. In addition, potassium showed promise as an independent prognostic biomarker that predicted both in-hospital mortality, with an AUC of 71.51% [95% Cl: 69.51%, 73.50%], and AA with AUC of 63.6% [95% Cl: 58.86%, 68.34%]. Within the test cohort, an increase of 1 mEq/L potassium was associated with an in-hospital mortality risk of 1.40 [95% CI: 1.14, 1.73] and a risk of new onset of AA of 1.55 [95% CI: 1.25, 1.93]. This cross-sectional study suggests that biomarkers can be used as prognostic variables for in-hospital mortality and onset of AA among hospitalized COVID-19 patients.

Biodegradable, Biocompatible, and Implantable Multifunctional Sensing Platform for Cardiac Monitoring

Cardiac monitoring after heart surgeries is crucial for health maintenance and detecting postoperative complications early. However, current methods like rigid implants have limitations, as they require performing second complex surgeries for removal, increasing infection and inflammation risks, thus prompting research for improved sensing monitoring technologies. Herein, we introduce a nanosensor platform that is biodegradable, biocompatible, and integrated with multifunctions, suitable for use as implants for cardiac monitoring. The device has two electrochemical biosensors for sensing lactic acid and pH as well as a pressure sensor and a chemiresistor array for detecting volatile organic compounds. Its biocompatibility with myocytes has been tested in vitro, and its biodegradability and sensing function have been proven with ex vivo experiments using a three-dimensional (3D)-printed heart model and 3D-printed cardiac tissue patches. Moreover, an artificial intelligence-based predictive model was designed to fuse sensor data for more precise health assessment, making it a suitable candidate for clinical use. This sensing platform promises impactful applications in the realm of cardiac patient care, laying the foundation for advanced life-saving developments.

Protocol to fabricate wearable stretchable microneedle-based sensors

Creating highly stretchable and robust electrodes while retaining conductivity and stability is challenging. Furthermore, combining these elastic parts with rigid ones brings its own problems due to the discrepancy in firmness between the flexible patches and rigid constructions. Here, we present a protocol to create a stable, conductive, and flexible microneedle sensor patch. We describe steps for using polystyrene-block-polyisoprene-block-polystyrene with silver nanowires, besides fabricating rigid microneedles and combining them together using a thickness-gradient strategy. For complete details on the use and execution of this protocol, please refer to Zheng et al. (2022).1.