Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions.

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.

Self-Sustaining Triboelectric Nanosensors for Real-Time Urine Analysis in Smart Toilets.

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 (R2), supported by artificial neural networks. Upon exposure to urine samples containing elevated levels of Na+, K+, and NH4 +, 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.

Tailor-Made Gold Nanomaterials for Applications in Soft Bioelectronics and Optoelectronics.

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.

Non-contact immunological signaling for highly-efficient regulation of the transcriptional map of human monocytes.

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.

Mobile Diagnostic Clinics.

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.

Volatilomic profiles of gastric juice in gastric cancer patients.

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.

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.

Anti-Wound Dehiscence and Antibacterial Dressing with Highly Efficient Self-Healing Feature for Guided Bone Regeneration Wound Closure.

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.

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.

A 10-micrometer-thick nanomesh-reinforced gas-permeable hydrogel skin sensor for long-term electrophysiological monitoring.

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.

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

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.

Facile Graphene Oxide Modification Method via Hydroxyl-yne Click Reaction for Ultrasensitive and Ultrawide Monitoring Pressure Sensors.

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.

Porous SnO2 nanosheets for room temperature ammonia sensing in extreme humidity.

Gas sensors based on tin dioxide (SnO2) for the detection of ammonia (NH3) 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 SnO2 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 SnO2 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 SnO2-based chemiresistive sensors. Its exceptional sensing abilities were credited to its optimal crystallinity, specific surface area, defects, chemisorbed oxygen, and porous structure. NH3-TPD measurements and computational simulations were employed to understand the ammonia interaction with atomistic details on the SnO2 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 SnO2-centered sensors, making it feasible to detect ammonia with enhanced precision, discrimination, dependability, and velocity for probable usages in medical diagnostics and ecological surveillance.

Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions.

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 Detection of Colorectal Cancer through Machine Learning-Based Breath Sensor Analysis.

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.

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.

Soft Bioelectronics for Therapeutics.

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.

Continuous Monitoring of Psychosocial Stress by Non-Invasive Volatilomics.

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.

Tailoring Food Biopolymers into Biogels for Regenerative Wound Healing and Versatile Skin Bioelectronics.

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.

Breath Volatile Organic Compounds in Surveillance of Gastric Cancer Patients following Radical Surgical Management.

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.