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Interleukin-6 (IL-6) plays a pivotal role in the inflammatory response of diabetic wounds, providing critical insights for clinicians in the development of personalized treatment strategies. However, the low concentration of IL-6 in biological samples, coupled with the presence of numerous interfering substances, poses a significant challenge for its rapid and accurate detection. Herein, we present a dual-mode microfluidic platform integrating electrochemical (EC) and surface-enhanced Raman spectroscopy (SERS) to achieve the timely and highly reliable quantification of IL-6. Efficient binding between IL-6 and antibody-conjugated SERS nanoprobes is obtained through a square-wave micromixer with nonleaky obstacles, forming sandwich immunocomplexes with IL-6 capture antibodies on the working electrode in the detection area, enabling acquisition of both EC and SERS signals. This microfluidic platform demonstrates excellent selectivity and sensitivity, with detection limits of 0.085 and 0.047 pg/mL for EC and SERS modes, respectively. Importantly, by incorporating a neural network (NN) with a self-attention (SA) mechanism to evaluate the relative weights of data from both modes, the platform achieves a quantitative accuracy of up to 99.8% across a range of 0.05-1000 pg/mL, demonstrating significant performance at low concentrations. Moreover, the NN-enhanced dual-mode microfluidic platform effectively detects IL-6 in diabetic wound exudates with results that align closely with clinical data. This integrated dual-mode microfluidic platform offers promising potential for the rapid and accurate detection of cytokines.

Organoids mimic human organ function, offering insights into development and disease. However, non-destructive, real-time monitoring is lacking, as traditional methods are often costly, destructive, and low-throughput. In this article, a non-destructive chemical tomographic strategy is presented for decoding cyto-proteo-genomics of organoid using volatile signaling molecules, hereby, Volatile Organic Compounds (VOCs), to indicate metabolic activity and development of organoids. Combining a hierarchical design of graphene-based sensor arrays with AI-driven analysis, this method maps VOC spatiotemporal distribution and generate detailed digital profiles of organoid morphology and proteo-genomic features. Lens- and label-free, it avoids phototoxicity, distortion, and environmental disruption. Results from testing organoids with the reported chemical tomography approach demonstrate effective differentiation between cyto-proteo-genomic profiles of normal and diseased states, particularly during dynamic transitions such as epithelial-mesenchymal transition (EMT). Additionally, the reported approach identifies key VOC-related biochemical pathways, metabolic markers, and pathways associated with cancerous transformations such as aromatic acid degradation and lipid metabolism. This real-time, non-destructive approach captures subtle genetic and structural variations with high sensitivity and specificity, providing a robust platform for multi-omics integration and advancing cancer biomarker discovery., (© 2025 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.)

Gastric cancer remains a leading cause of cancer-related mortality, requiring the urgent development of innovative diagnostic tools for early detection. This study presents an integrated infrared spectroscopic electronic nose system, a novel device that combines infrared (IR) spectroscopy and electronic nose (eNose) concepts for analyzing volatile organic compounds (VOCs) in exhaled breath. This system was calibrated using relevant gas mixtures and then tested during a feasibility study involving 26 gastric cancer patients and 32 healthy controls using chemometric analyses to distinguish between exhaled breath profiles. The obtained results demonstrated that the integration of IR spectroscopy and eNose technologies significantly enhanced the accuracy of VOCs fingerprinting via principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA). Distinct differences between the study groups were revealed with an accuracy of prediction of 0.96 in exhaled breath samples. This combined system offers a high sensitivity and specificity and could potetially facilitate rapid on-site testing rendering the technology an accessible option for early screening particularly in underserved populations.

Healthcare has undergone a revolutionary shift with the advent of smart technologies, and smart toilets (STs) are among the innovative inventions offering non-invasive continuous health monitoring. The present technical challenges toward this development include limited sensitivity of integrated sensors, poor stability, slow response and the requirement external energy supply alongside manual sample collection. In this article, triboelectric nanosensor array (TENSA) is introduced featuring electrodes crafted from laser-induced 3D graphene with functional polymers like polystyrene, polyimide, and polycaprolactone for real-time urine analysis while generating 50 volts output via urine droplet-based triboelectrification. Though modulating interfacial double-layer capacitance, these sensors exhibit exceptional sensitivity and selectivity in detecting a broad spectrum of urinary biomarkers, including ions, glucose, and urea with a classification precision of 95% and concentration identification accuracy of up to 0.97 (R 2 ), supported by artificial neural networks. Upon exposure to urine samples containing elevated levels of Na + , K + , and NH 4 + , a notable decrease (ranging from 32% to 68%) is observed in output voltages. Conversely, urea induces an increase up to 13%. Experimental validation confirms the stability, robustness, reliability, and reproducibility of TENSA, representing a significant advancement in healthcare technology, offering the potential for improved disease management and prevention strategies., (© 2024 The Author(s). Small published by Wiley‐VCH GmbH.)

Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

In modern nanoscience and nanotechnology, gold nanomaterials are indispensable building blocks that have demonstrated a plethora of applications in catalysis, biology, bioelectronics, and optoelectronics. Gold nanomaterials possess many appealing material properties, such as facile control over their size/shape and surface functionality, intrinsic chemical inertness yet with high biocompatibility, adjustable localized surface plasmon resonances, tunable conductivity, wide electrochemical window, etc. Such material attributes have been recently utilized for designing and fabricating soft bioelectronics and optoelectronics. This motivates to give a comprehensive overview of this burgeoning field. The discussion of representative tailor-made gold nanomaterials, including gold nanocrystals, ultrathin gold nanowires, vertically aligned gold nanowires, hard template-assisted gold nanowires/gold nanotubes, bimetallic/trimetallic gold nanowires, gold nanomeshes, and gold nanosheets, is begun. This is followed by the description of various fabrication methodologies for state-of-the-art applications such as strain sensors, pressure sensors, electrochemical sensors, electrophysiological devices, energy-storage devices, energy-harvesting devices, optoelectronics, and others. Finally, the remaining challenges and opportunities are discussed., (© 2024 Wiley‐VCH GmbH.)

This article reviews the revolutionary impact of emerging technologies and artificial intelligence (AI) in reshaping modern healthcare systems, with a particular focus on the implementation of mobile diagnostic clinics. It presents an insightful analysis of the current healthcare challenges, including the shortage of healthcare workers, financial constraints, and the limitations of traditional clinics in continual patient monitoring. The concept of "Mobile Diagnostic Clinics" is introduced as a transformative approach where healthcare delivery is made accessible through the incorporation of advanced technologies. This approach is a response to the impending shortfall of medical professionals and the financial and operational burdens conventional clinics face. The proposed mobile diagnostic clinics utilize digital health tools and AI to provide a wide range of services, from everyday screenings to diagnosis and continual monitoring, facilitating remote and personalized care. The article delves into the potential of nanotechnology in diagnostics, AI's role in enhancing predictive analytics, diagnostic accuracy, and the customization of care. Furthermore, the article discusses the importance of continual, noninvasive monitoring technologies for early disease detection and the role of clinical decision support systems (CDSSs) in personalizing treatment guidance. It also addresses the challenges and ethical concerns of implementing these advanced technologies, including data privacy, integration with existing healthcare infrastructure, and the need for transparent and bias-free AI systems.

Guided bone regeneration (GBR) is a well-established technique for preserving and enhancing alveolar ridge structures. Success in GBR relies on fulfilling the Primary wound closure, Angiogenesis, Space maintenance, and Stability (PASS) principles. Conventional methods, involving titanium meshes and sutures, have drawbacks, including the need for secondary removal and customization challenges. To address these issues, an innovative multifunctional GBR dressing (MGD) based on self-healing elastomer (PUIDS) is introduced. MGD provides sutureless wound closure, prevents food particle accumulation, and maintains a stable environment for bone growth. It offers biocompatibility, bactericidal properties, and effectiveness in an oral GBR model. In summary, MGD provides a reliable, stable osteogenic environment for GBR, aligning with PASS principles and promoting superior post-surgery bone regeneration., (© 2024 The Authors. Advanced Healthcare Materials published by Wiley‐VCH GmbH.)

Volatilomics is a powerful tool capable of providing novel biomarkers for the diagnosis of gastric cancer. The main objective of this study was to characterize the volatilomic signatures of gastric juice in order to identify potential alterations induced by gastric cancer. Gas chromatography with mass spectrometric detection, coupled with headspace solid phase microextraction as the pre-concentration technique, was used to identify volatile organic compounds (VOCs) released by gastric juice samples collected from 78 gastric cancer patients and two cohorts of controls (80 and 96 subjects) from four different locations (Latvia, Ukraine, Brazil, and Colombia). 1440 distinct compounds were identified in samples obtained from patients and 1422 in samples provided by controls. However, only 6% of the VOCs exhibited an incidence higher than 20%. Amongst the volatiles emitted, 18 showed differences in their headspace concentrations above gastric juice of cancer patients and controls. Ten of these (1-propanol, 2,3-butanedione, 2-pentanone, benzeneacetaldehyde, 3-methylbutanal, butylated hydroxytoluene, 2-pentyl-furan, 2-ethylhexanal, 2-methylpropanal and phenol) appeared at significantly higher levels in the headspace of the gastric juice samples obtained from patients; whereas, eight species showed lower abundance in patients than found in controls. Given that the difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes or pathways, the former set can be considered potential biomarkers for gastric cancer, which may assist in developing non-invasive breath tests for the diagnosis of this disease. Further studies are required to elucidate further the mechanisms that underlie the changes in the volatilomic profile as a result of gastric cancer., (© 2024 IOP Publishing Ltd.)

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., Competing Interests: The authors declare no competing interests., (Copyright: © 2024 Omar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Enhancing the durability and functionality of existing materials through sustainable pathways and appropriate structural design represents a time- and cost-effective strategy for the development of advanced wearable devices. Herein, a facile graphene oxide (GO) modification method via the hydroxyl-yne click reaction is present for the first time. By the click coupling between propiolate esters and hydroxyl groups on GO under mild conditions, various functional molecules are successfully grafted onto the GO. The modified GO is characterized by FTIR, XRD, TGA, XPS, and contact angle, proving significantly improved dispersibility in various solvents. Besides the high efficiency, high selectivity, and mild reaction conditions, this method is highly practical and accessible, avoiding the need for prefunctionalizations, metals, or toxic reagents. Subsequently, a rGO-PDMS sponge-based piezoresistive sensor developed by modified GO-P2 as the sensitive material exhibits impressive performance: high sensitivity (335 kPa -1 , 0.8-150 kPa), wide linear range (>500 kPa), low detection limit (0.8 kPa), and long-lasting durability (>5000 cycles). Various practical applications have been demonstrated, including body joint movement recognition and real-time monitoring of subtle movements. These results prove the practicality of the methodology and make the rGO-PDMS sponge-based pressure sensor a real candidate for a wide array of wearable applications.

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.

Hydrogel-enabled skin bioelectronics that can continuously monitor health for extended periods is crucial for early disease detection and treatment. However, it is challenging to engineer ultrathin gas-permeable hydrogel sensors that can self-adhere to the human skin for long-term daily use (>1 week). Here, we present a ~10-micrometer-thick polyurethane nanomesh-reinforced gas-permeable hydrogel sensor that can self-adhere to the human skin for continuous and high-quality electrophysiological monitoring for 8 days under daily life conditions. This research involves two key steps: (i) material design by gelatin-based thermal-dependent phase change hydrogels and (ii) robust thinness geometry achieved through nanomesh reinforcement. The resulting ultrathin hydrogels exhibit a thickness of ~10 micrometers with superior mechanical robustness, high skin adhesion, gas permeability, and anti-drying performance. To highlight the potential applications in early disease detection and treatment that leverage the collective features, we demonstrate the use of ultrathin gas-permeable hydrogels for long-term, continuous high-precision electrophysiological monitoring under daily life conditions up to 8 days.

Gas sensors based on tin dioxide (SnO 2 ) for the detection of ammonia (NH 3 ) have become commercially available for environmental monitoring due to their reactive qualities when exposed to different gaseous pollutants. Nevertheless, their implementation in the medical field has been hindered by certain inherent drawbacks, such as needing to operate at high temperatures, lack of selectivity, unreliable operation under high-humidity conditions, and a lower detection limit. To counter these issues, this study created 2D nanosheets of SnO 2 through an optimized solvothermal method. It was found that tuning the precursor solution's pH to either neutral or 14 led to aggregated or distributed, uniform-size nanosheets with a higher crystallinity, respectively. Remarkably, the SnO 2 nanosheet sensor (SNS-14) displayed a much lower response to water molecules and specific reactivity to ammonia even when subjected to reducing and oxidizing agents at 25 °C due to the micropores and chemisorbed oxygen on the nanosheets. Furthermore, the SNS-14 was seen to have the highest sensitivity to ammonia at 100 ppm, with rapid response (8 s) and recovery times (55 s) even at a high relative humidity of 70%. Its theoretical detection limit was recorded to be 64 ppt, better than any of the earlier SnO 2 -based chemiresistive sensors. Its exceptional sensing abilities were credited to its optimal crystallinity, specific surface area, defects, chemisorbed oxygen, and porous structure. NH 3 -TPD measurements and computational simulations were employed to understand the ammonia interaction with atomistic details on the SnO 2 nanosheet surface. A real time breath sensing experiment was simulated to test the efficacy of the sensor. Reaching this advancement is an achievement in bypassing past boundaries of SnO 2 -centered sensors, making it feasible to detect ammonia with enhanced precision, discrimination, dependability, and velocity for probable usages in medical diagnostics and ecological surveillance.

Online monitoring of prognostic biomarkers is critically important when diagnosing disorders and assessing individuals' health, especially for chronic and infectious diseases. Despite this, current diagnosis techniques are time-consuming, labor-intensive, and performed offline. In this context, developing wearable devices for continuous measurements of multiple biomarkers from body fluids has considerable advantages including availability, rapidity, convenience, and minimal invasiveness over the conventional painful and time-consuming tools. However, there is still a significant challenge in powering these devices over an extended period, especially for applications that require continuous and long-term health monitoring. Herein, a new freestanding, wearable, multifunctional microneedle-based extended gate field effect transistor biosensor is fabricated for online detection of multiple biomarkers from the interstitial fluid including sodium, calcium, potassium, and pH along with excellent electrical response, reversibility, and precision. In addition, a hybrid powering system of triboelectric nanogenerator and solar cell was developed for creating a freestanding, closed-loop platform for continuous charging of the device's battery and integrated with an Internet of Things technology to broadcast the measurements online, suggesting a stand-alone, stable multifunctional tool which paves the way for advanced practical personalized health monitoring and diagnosis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

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 ., Competing Interests: Declaration of interests The Technion - Israel Institute of Technology holds a patent for this invention (TECH/092 USP). H.H., Y.Z., and R.O. are the inventors of the patent., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.

The search for inexpensive and all-electric tunable methods for portable and fast recognition and discrimination between various chiral enantiomers, mainly those found in the gas phase, has been one of the most challenging tasks in the field of analytical chemistry. The current article reports on a chiral sensitive electric architecture (CSEA) of a helical polyaniline (PANI)@carbon nanotube (CNT) hybrid quantum-wire based field effect transistor (FET) platform. The CSEA architecture exhibits gate-controlled-channel-chirality modulation for the selective distinction of Limonene (S(+)/R(-)) at ≈12 V intervals. Typical gate-modulated selectivity of S(+)-Limonene and R(-)-Limonene using two opposite helically turned hybrids, namely as, S-PANI@CNT and R-PANI@CNT are 6.5 and 2.8, respectively. Theoretical analysis and modelling relates the gas-phase chiral quantum probe with spin-channel modulation in CNT by Rashba spin-orbit interaction. This is achieved by applied gate voltage, CNT's unique curved surface, adsorbed chiral adatom induced scattering center on the curved graphitic lattice and helicoid field from a synthetically prepared helical PANI@CNT hybrid interface., (© 2022 Wiley-VCH GmbH.)

The rapid development of wearable biosensing calls for next-generation devices that allow continuous, real-time, and painless monitoring of health status along with responsive medical treatment. Microneedles have exhibited great potential for the direct access of dermal interstitial fluid (ISF) in a minimally invasive manner. Recent studies of microneedle-based devices have evolved from conventional off-line detection to multiplexed, wireless, and integrated sensing. In this review, the classification and fabrication techniques of microneedles are first introduced, and then the representative examples of microneedles for transdermal monitoring with different sensing modalities are summarized. State-of-the-art advances in therapeutic and closed-loop systems are presented to formulate guidelines for the development of next-generation microneedle-based healthcare platforms. The potential challenges and prospects are discussed to pave a new avenue toward pragmatic applications in the real world., (© 2023 Wiley-VCH GmbH.)

Colorectal cancer (CRC) is the third most common malignancy and the second most common cause of cancer-related deaths worldwide. While CRC screening is already part of organized programs in many countries, there remains a need for improved screening tools. In recent years, a potential approach for cancer diagnosis has emerged via the analysis of volatile organic compounds (VOCs) using sensor technologies. The main goal of this study was to demonstrate and evaluate the diagnostic potential of a table-top breath analyzer for detecting CRC. Breath sampling was conducted and CRC vs. non-cancer groups (105 patients with CRC, 186 non-cancer subjects) were included in analysis. The obtained data were analyzed using supervised machine learning methods (i.e., Random Forest, C4.5, Artificial Neural Network, and Naïve Bayes). Superior accuracy was achieved using Random Forest and Evolutionary Search for Features (79.3%, sensitivity 53.3%, specificity 93.0%, AUC ROC 0.734), and Artificial Neural Networks and Greedy Search for Features (78.2%, sensitivity 43.3%, specificity 96.5%, AUC ROC 0.735). Our results confirm the potential of the developed breath analyzer as a promising tool for identifying and categorizing CRC within a point-of-care clinical context. The combination of MOX sensors provided promising results in distinguishing healthy vs. diseased breath samples. Its capacity for rapid, non-invasive, and targeted CRC detection suggests encouraging prospects for future clinical screening applications.

Soft bioelectronics play an increasingly crucial role in high-precision therapeutics due to their softness, biocompatibility, clinical accuracy, long-term stability, and patient-friendliness. In this review, we provide a comprehensive overview of the latest representative therapeutic applications of advanced soft bioelectronics, ranging from wearable therapeutics for skin wounds, diabetes, ophthalmic diseases, muscle disorders, and other diseases to implantable therapeutics against complex diseases, such as cardiac arrhythmias, cancer, neurological diseases, and others. We also highlight key challenges and opportunities for future clinical translation and commercialization of soft therapeutic bioelectronics toward personalized medicine.

Stress is becoming increasingly commonplace in modern times, making it important to have accurate and effective detection methods. Currently, detection methods such as self-evaluation and clinical questionnaires are subjective and unsuitable for long-term monitoring. There have been significant studies into biomarkers such as HRV, cortisol, electrocardiography, and blood biomarkers, but the use of multiple electrodes for electrocardiography or blood tests is impractical for real-time stress monitoring. To this end, there is a need for non-invasive sensors to monitor stress in real time. This study looks at the possibility of using breath and skin VOC fingerprinting as stress biomarkers. The Trier social stress test (TSST) was used to induce acute stress and HRV, cortisol, and anxiety levels were measured before, during, and after the test. GC-MS and sensor array were used to collect and measure VOCs. A prediction model found eight different stress-related VOCs with an accuracy of up to 78%, and a molecularly capped gold nanoparticle-based sensor revealed a significant difference in breath VOC fingerprints between the two groups. These stress-related VOCs either changed or returned to baseline after the stress induction, suggesting different metabolic pathways at different times. A correlation analysis revealed an association between VOCs and cortisol levels and a weak correlation with either HRV or anxiety levels, suggesting that VOCs may include complementary information in stress detection. This study shows the potential of VOCs as stress biomarkers, paving the way into developing a real-time, objective, non-invasive stress detection tool for well-being and early detection of stress-related diseases.

An increasing utilization of wound-related therapeutic materials and skin bioelectronics urges the development of multifunctional biogels for personal therapy and health management. Nevertheless, conventional dressings and skin bioelectronics with single function, mechanical mismatches, and impracticality severely limit their widespread applications in clinical. Herein, we explore a gelling mechanism, fabrication method, and functionalization for broadly applicable food biopolymers-based biogels that unite the challenging needs of elastic yet injectable wound dressing and skin bioelectronics in a single system. We combine our biogels with functional nanomaterials, such as cuttlefish ink nanoparticles and silver nanowires, to endow the biogels with reactive oxygen species scavenging capacity and electrical conductivity, and finally realized the improvement in diabetic wound microenvironment and the monitoring of electrophysiological signals on skin. This line of research work sheds light on preparing food biopolymers-based biogels with multifunctional integration of wound treatment and smart medical treatment., (© 2023. The Author(s).)

This article reports on a molecular-spin-sensitive-antenna (MSSA) that is based on stacked layers of organically functionalized graphene on a fibrous helical cellulose network for carrying out spatiotemporal identification of chiral enantiomers. The MSSA structures combine three complementary features: (i) chiral separation via a helical quantum sieve for chiral trapping, (ii) chiral recognition by a synthetically implanted spin-sensitive center in a graphitic lattice; and (iii) chiral selectivity by a chirality-induced-spin mechanism that polarizes the local electronic band-structure in graphene through chiral-activated Rashba spin-orbit interaction field. Combining the MSSA structures with decision-making principles based on neuromorphic artificial intelligence shows fast, portable, and wearable spectrometry for the detection and classification of pure and a mixture of chiral molecules, such as butanol (S and R), limonene (S and R), and xylene isomers, with 95-98% accuracy. These results can have a broad impact where the MSSA approach is central as a precautionary risk assessment against potential hazards impacting human health and the environment due to chiral molecules; furthermore, it acts as a dynamic monitoring tool of all parts of the chiral molecule life cycles., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

As of today, there is a lack of a perfect non-invasive test for the surveillance of patients for potential relapse following curative treatment. Breath volatile organic compounds (VOCs) have been demonstrated to be an accurate diagnostic tool for gastric cancer (GC) detection; here, we aimed to prove the yield of the markers in surveillance, i.e., following curative surgical management. Patients were sampled in regular intervals before and within 3 years following curative surgery for GC; gas chromatography-mass spectrometry (GC-MS) and nanosensor technologies were used for the VOC assessment. GC-MS measurements revealed a single VOC (14b-Pregnane) that significantly decreased at 12 months, and three VOCs (Isochiapin B, Dotriacontane, Threitol, 2-O-octyl-) that decreased at 18 months following surgery. The nanomaterial-based sensors S9 and S14 revealed changes in the breath VOC content 9 months after surgery. Our study results confirm the cancer origin of the particular VOCs, as well as suggest the value of breath VOC testing for cancer patient surveillance, either during the treatment phase or thereafter, for potential relapse.

The sustainable operation of wearable sensors plays an important role in continuous and longtime health monitoring. Conventional batteries, which are bulky and rigid, do not satisfy these requirements and, rather, cause additional economic burdens and environmental problems by regular replacement of power sources. This article provides a review on an alternative solution in the form of self-powered devices that can harvest energy from the surrounding environment to support the operation of the wearable sensor. The Review starts with an introduction of the self-powered triboelectric nanosensors (TENSs) and its two independent modules: the energy harvester and the sensing module. The Review continues with the TENS-related bioinspired designs for wearable applications, while providing a bird's-eye view of their characteristics and applications. The ongoing challenges and prospects for providing personal healthcare with self-powered TENS are presented and discussed.

Liquid biopsy is seen as a prospective tool for cancer screening and tracking. However, the difficulty lies in effectively sieving, isolating, and overseeing cancer biomarkers from the backdrop of multiple disrupting cells and substances. The current study reports on the ability to perform liquid biopsy without the need to physically filter and/or isolate the cancer cells per se. This has been achieved through the detection and classification of volatile organic compounds (VOCs) emitted from the cancer cells found in the headspace of blood or urine samples or a combined data set of both. Spectrometric analysis shows that blood and urine contain complementary or overlapping VOC information on kidney cancer, gastric cancer, lung cancer, and fibrogastroscopy subjects. Based on this information, a nanomaterial-based chemical sensor array in conjugation with machine learning as well as data fusion of the signals achieved was carried out on various body fluids to assess the VOC profiles of cancer. The detection of VOC patterns by either Gas Chromatography-Mass Spectrometry (GC-MS) analysis or our sensor array achieved >90% accuracy, >80% sensitivity, and >80% specificity in different binary classification tasks. The hybrid approach, namely, analyzing the VOC datasets of blood and urine together, contributes an additional discrimination ability to the improvement (>3%) of the model's accuracy. The contribution of the hybrid approach for an additional discrimination ability to the improvement of the model's accuracy is examined and reported.

The different immune system cells communicate and coordinate a response using a complex and evolved language of cytokines and chemokines. These cellular interactions carry out multiple functions in distinct cell types with numerous developmental outcomes. Despite the plethora of different cytokines and their cognate receptors, there is a restricted number of signal transducers and activators to control immune responses. Herein, we report on a new class of immunomodulatory signaling molecules based on volatile molecules (VMs, namely, volatile organic compounds [VOCs]), by which they can affect and/or control immune cell behavior and transcriptomic profile without any physical contact with other cells. The study demonstrates the role of VMs by analyzing non-contact cell communication between normal and cancerous lung cells and U937 monocytes, which are key players in the tumor microenvironment. Integrated transcriptome and proteome analyses showed the suggested regulatory role of VMs released from normal and cancer cells on neighboring monocytes in several molecular pathways, including PI3K/AKT, PPAR, and HIF-1. Presented data provide an initial platform for a new class of immunomodulatory molecules that can potentially mirror the genomic and proteomic profile of cells, thereby paving the way toward non-invasive immunomonitoring., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors. Bioengineering & Translational Medicine published by Wiley Periodicals LLC on behalf of The American Institute of Chemical Engineers.)

Stress is a leading cause of several disease types, yet it is underdiagnosed as current diagnostic methods are mainly based on self-reporting and interviews that are highly subjective, inaccurate, and unsuitable for monitoring. Although some physiological measurements exist ( e.g ., heart rate variability and cortisol), there are no reliable biological tests that quantify the amount of stress and monitor it in real time. In this article, we report a novel way to measure stress quickly, noninvasively, and accurately. The overall detection approach is based on measuring volatile organic compounds (VOCs) emitted from the skin in response to stress. Sprague Dawley male rats ( n = 16) were exposed to underwater trauma. Sixteen naive rats served as a control group ( n = 16). VOCs were measured before, during, and after induction of the traumatic event, by gas chromatography linked with mass spectrometry determination and quantification, and an artificially intelligent nanoarray for easy, inexpensive, and portable sensing of the VOCs. An elevated plus maze during and after the induction of stress was used to evaluate the stress response of the rats, and machine learning was used for the development and validation of a computational stress model at each time point. A logistic model classifier with stepwise selection yielded a 66-88% accuracy in detecting stress with a single VOC (2-hydroxy-2-methyl-propanoic acid), and an SVM (support vector machine) model showed a 66-72% accuracy in detecting stress with the artificially intelligent nanoarray. The current study highlights the potential of VOCs as a noninvasive, automatic, and real-time stress predictor for mental health.

Extracellular vesicles (EVs) are nanoscale membrane vesicles, which contain a wide variety of cargo such as proteins, miRNAs, and lipids. A growing body of evidence suggests that EVs are promising biomarkers for disease diagnosis and therapeutic strategies. Although the excellent clinical value, their use in personalized healthcare practice is not yet feasible due to their highly heterogeneous nature. Taking the difficulty of isolation and the small size of EVs into account, the characterization of EVs at a single-particle level is both imperative and challenging. In a bid to address this critical point, more research has been directed into a microfluidic platform because of its inherent advantages in sensitivity, specificity, and throughput. This review discusses the biogenesis and heterogeneity of EVs and takes a broad view of state-of-the-art advances in microfluidics-based EV research, including not only EV separation, but also the single EV characterization of biophysical detection and biochemical analysis. To highlight the advantages of microfluidic techniques, conventional technologies are included for comparison. The current status of artificial intelligence (AI) for single EV characterization is then presented. Furthermore, the challenges and prospects of microfluidics and its combination with AI applications in single EV characterization are also discussed. In the foreseeable future, recent breakthroughs in microfluidic platforms are expected to pave the way for single EV analysis and improve applications for precision medicine., (© 2023 Wiley-VCH GmbH.)

Skin-mountable electronics are considered to be the future of the next generation of portable electronics, due to their softness and seamless integration with human skin. However, impermeable materials limit device comfort and reliability for long-term, continuous usage. The recent emergence of permeable skin-mountable electronics has attracted tremendous attention in the soft electronics field. Herein, we provide a comprehensive and systematic review of permeable skin-mountable electronics. Typical porous materials and structures are first highlighted, followed by discussion of important device properties. Then, we review the latest representative applications of breathable skin-mountable electronics, such as bioelectrical sensors, temperature sensors, humidity and hydration sensors, strain and pressure sensors, and energy harvesting and storage devices. Finally, a conclusion and future directions for permeable skin electronics are provided.

The main milestones in the exploration and validation of cancer breathprint for the advancement of personalised diagnosis and medicine are summarised here, with a special attention to the appraisal and translation of the accumulating knowledge from the laboratory to the Point-of-Care phase. An outlook into the opportunities of the use of breathprints and their wider availability for healthcare is offered., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Xylene is one of the representative indoor pollutants, even in ppb levels, that affect human health directly. Due to the non-polar and less reactive nature of xylene, its room temperature detection is challenging. This work demonstrates a metallic tin-doped Sn-SnO 2 nanocomposite under controlled pH conditions via a simple solvothermal route. The Sn nanoparticles are uniformly distributed inside the SnO 2 nanospheres of ∼70 nm with a high specific surface area of 118.8 m 2 /g. The surface of the Sn-SnO 2 nanocomposite exhibits strong affinity toward benzene, toluene, ethylbenzene, and xylene (BTEX) compared to other polar volatile organic compounds (VOCs) such as ethanol, acetone, isopropyl alcohol, formaldehyde, and chloroform tested in this study. The sensor's response is highest for xylene among BTEX molecules. Under ambient room temperature conditions, the sensor exhibits a linear response to xylene in the 1-100 ppm range with a sensitivity of ∼255% at 60 ppm within ∼1.5 s and recovers in ∼40 s. The sensor is hardly affected by humidity variations (40-70%), leading to enhanced reliability and repeatability under dynamic environmental conditions. The meso and microporous nanosphere morphology act as a nanocontainer for non-polar VOCs to diffuse inside the nanostructures, providing easy accessibility. The metallic Sn increases the affinity for less reactive xylene at room temperature. Thus, the nanocatalytic Sn-SnO 2 nanocomposite is an active gas/VOC sensing material and provides an effective solution for sensing major indoor pollutants under humid conditions.

Background: Volatilomics is a powerful tool capable of providing novel biomarkers for medical diagnosis and therapy monitoring. The objective of this study is to identify potential volatile biomarkers of gastric cancer., Methods: The volatilomic signatures of gastric tissues obtained from two distinct populations were investigated using gas chromatography with mass spectrometric detection., Results: Amongst the volatiles emitted, nineteen showed differences in their headspace concentrations above the normal and cancer tissues in at least one population of patients. Headspace levels of seven compounds (hexanal, nonanal, cyclohexanone, 2-nonanone, pyrrole, pyridine, and phenol) were significantly higher above the cancer tissue, whereas eleven volatiles (ethyl acetate, acetoin, 2,3-butanedione, 3-methyl-1-butanol, 2-pentanone, γ-butyrolactone, DL-limonene, benzaldehyde, 2-methyl-1-propanol, benzonitrile, and 3-methyl-butanal) were higher above the non-cancerous tissue. One compound, isoprene, exhibited contradictory alterations in both cohorts. Five compounds, pyridine, ethyl acetate, acetoin, 2,3-butanedione, and 3-methyl-1-butanol, showed consistent cancer-related changes in both populations., Conclusions: Pyridine is found to be the most promising biomarker candidate for detecting gastric cancer. The difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes, or pathways. The results of this study confirm that the chemical fingerprint formed by volatiles in gastric tissue is altered by gastric cancer.

The design and characterization of spatiotemporal nano-/micro-structural arrangement that enable real-time and wide-spectrum molecular analysis is reported and demonestrated in new horizons of biomedical applications, such as wearable-spectrometry, ultra-fast and onsite biopsy-decision-making for intraoperative surgical oncology, chiral-drug identification, etc. The spatiotemporal sesning arrangement is achieved by scalable, binder-free, functionalized hybrid spin-sensitive (<↑| or <↓|) graphene-ink printed sensing layers on free-standing films made of porous, fibrous, and naturally helical cellulose networks in hierarchically stacked geometrical configuration (HSGC). The HSGC operates according to a time-space-resolved architecture that modulate the mass-transfer rate for separation, eluation and detection of each individual compound within a mixture of the like, hereby providing a mass spectrogram. The HSGC could be used for a wide range of applictions, including fast and real-time spectrogram generator of volatile organic compounds during liquid-biopsy, without the need of any immunochemistry-staining and complex power-hungry cryogenic machines; and wearable spectrometry that provide spectral signature of molecular profiles emiited from skin in the course of various dietry conditions., (© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Volatile organic compounds (VOCs) in urine are valuable biomarkers for noninvasive disease diagnosis. Herein, a facile coordination-driven modular assembly strategy is used for developing a library of gas-sensing materials based on porous MXene frameworks (MFs). Taking advantage of modules with diverse composition and tunable structure, our MFs-based library can provide more choices to satisfy gas-sensing demands. Meanwhile, the laser-induced graphene interdigital electrodes array and microchamber are laser-engraved for the assembly of a microchamber-hosted MF (MHMF) e-nose. Our MHMF e-nose possesses high-discriminative pattern recognition for simultaneous sensing and distinguishing of complex VOCs. Furthermore, with the MHMF e-nose being a plug-and-play module, a point-of-care testing (POCT) platform is modularly assembled for wireless and real-time monitoring of urinary volatiles from clinical samples. By virtue of machine learning, our POCT platform achieves noninvasive diagnosis of multiple diseases with a high accuracy of 91.7%, providing a favorable opportunity for early disease diagnosis, disease course monitoring, and relevant research.

Materials in nature feature versatile and programmable interactions to render macroscopic architectures with multiscale structural arrangements. By rationally combining metal-carboxylate and metal-organophosphate coordination interactions, Au 25 (MHA) 18 (MHA, 6-mercaptohexanoic acid) nanocluster self-assembled structural color coating films and phytic acid (PA)-metal coordination complexes are sequentially constructed on the surface of titanium implants. The Lewis acid-base coordination principle applies for these metal-organic coordination networks. The isotropic arrangement of nanoclusters with a short-range order is investigated via grazing incidence wide-angle X-ray scattering. The integration of robust M-O (M = Ti, Zr, Hf) and labile Cu-O coordination bonds with high connectivity of Au 25 (MHA) 18 nanoclusters enables these artificial photonic structures to achieve a combination of mechanical stability and bacteriostatic activity. Moreover, the colorless and transparent PA-metal complex layer allows the viewing of the structural color and surface wettability switching to hydrophilic and makes feasible the interfacial biomineralization of hydroxyapatite. Collectively, these modular metal-organic coordination-driven assemblies are predictive and rational material design strategies with tunable hierarchy and diversity. The complete metal-organic architectures will not only help improve the physicochemical properties of the bone-implant interface with synergistic antibacterial and osseointegration activities but also can boost surface engineering of medical metal implants.

Herein, a multifunctional bilayer wound patch is developed by integrating a debonding-on-demand polymeric tissue adhesive (DDPTA) with an ionic conducting elastomer (ICE). As a skin adhesive layer, the DDPTA is soft and adherent at skin temperature but hard and non-tacky when cooled, so it provides unique temperature-triggered quick adhesion and non-forced detachment from the skin. During use, the dense surface of the DDPTA prevents blood infiltration and reduces unnecessary blood loss with gentle pressing. Moreover, its hydrophobic matrix helps to repel blood and prevents the formation of clots, thus precluding wound tearing during its removal. This unique feature enables the DDPTA to avoid the severe deficiencies of hydrophilic adhesives, providing a reliable solution for a wide range of secondary wound injuries. The DDPTA is versatile in that it can be covered with ICE to configure a DDPTA@ICE patch for initiating non-verbal communication systems by the fingers, leading toward sign language recognition and a remote clinical alarm system. This multifunctional wound patch with debonding-on-demand can promote a new style of tissue sealant for convenient clinical communication., (© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH.)

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer., (© 2021 The Authors. Advanced Healthcare Materials published by Wiley-VCH GmbH.)

Current methods for embryo selection are limited. This study assessed a novel method for the prediction of embryo developmental potential based on the analysis of volatile organic compounds (VOCs) emitted by embryo samples. The study included mice embryos monitored during the pre-implantation period. Four developmental stages of the embryos were tested, covering the period from 1 to 4 days after fecundation. In each stage, the VOCs released by the embryos were collected and examined by employing two different volatolomic techniques, gas-chromatography coupled to mass-spectrometry (GC-MS) and a nanoarray of chemical gas sensors. The GC-MS study revealed that the VOC patterns emanating from embryo samples had statistically different values at different stages of embryo development. The sensor nanoarray was capable of classifying the developmental stages of the embryos. The proposed volatolomics analysis approach for embryos presents a promising potential for predicting their developmental stage. In combination with conventional morphokinetic parameters, it could be effective as a predictive model for detecting metabolic shifts that affect embryo quality before preimplantation.