Prevalence, Antimicrobial Resistance, and Diversity of Campylobacter Isolated from U.S. Goat Feces: 2019 NAHMS Survey.

Goats are often asymptomatic carriers of Campylobacter, including the foodborne pathogen Campylobacter jejuni. Infections can have significant and economically detrimental health outcomes in both humans and animals. The primary objective of this study was to estimate the prevalence of Campylobacter in U.S. goat herds. Campylobacter species were isolated from 106 of 3,959 individual animals and from 42 of 277 goat operations that participated in fecal sample collection as part of the National Animal Health Monitoring System Goat 2019 study. Weighted animal-level prevalence was 2.3% (SE = 0.5%) and operation prevalence was 13.0% (SE = 3.2%). Animal-level prevalence ranged widely from 0 to 70.0%, however, 52.4% of positive operations (22/42) had only a single isolate. C. jejuni was the most frequently isolated species (68.9%; 73/106), followed by C. coli (29.3%, 31/106). A total of 46.2% (36/78) of viable isolates were pan-susceptible to 8 antimicrobials. Resistance to tetracycline (TET) was observed in 44.9% (35/78) of isolates, while 12.8% (10/78) were resistant to ciprofloxacin (CIP) and nalidixic acid (NAL). Among all isolates, a single resistance profile CIP-NAL-TET was observed in 3.8% (3/78) of isolates. A total of 35 unique sequence types (STs) were identified, 11 of which are potentially new. Multiple C. jejuni STs were observed in 48.1% (13/27) of positive operations. Goats with access to surface water, operations reporting antibiotics in the feed or water (excluding ionophores and coccidiostats), and operations reporting abortions and without postabortion management tasks had significantly greater odds of being Campylobacter positive. This snapshot of the U.S. goat population enriches the limited pool of knowledge on Campylobacter species presence in U.S. goats.

Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices.

Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.

Data recording of seizures in children with autism spectrum disorder: description and social validation of computer-assisted measurement.

Some children with autism spectrum disorder (ASD) experience seizures and associated staring episodes, loss of consciousness, weakened muscle tone, and myoclonic jerking. Data recording of seizure frequency, duration, and co-occurring behavior is necessary to document the effects of anti-epileptic medications, identify contextual influences on seizure expression, and differentiate seizures from other movement disorders. We describe the design and operation of a computer-assisted system for recording seizures among children with ASD in a social validity study that revealed uniform approval and acceptance of the system from practitioners, clinicians, and nurse (N = 22), parents (N = 11), and neurologists (N = 7). The objectives and benefits of targeting the social validity of technology-based seizure tracking are discussed are discussed.

Evaluation of North Sea saturation procedures through divers monitoring.

BACKGROUND: Saturation diving is a standard method of intervention for commercial diving during offshore operations. Current saturation procedures achieve a high level of safety with regards to decompression sickness but still put the divers under multiple stressors: 1) Environmental stress (long confinement, heat/cold, dense gases, high oxygen levels), 2) Work stress (muscular fatigue, psychological pressure, breathing equipment, etc.), 3) venous gas emboli associated with decompression, 4) Inflammation related to oxidative stress and microparticles. We present the results of a saturation divers monitoring campaign performed in the North Sea Danish sector, on the Tyra field, during 2022. The study was supported by TotalEnergies, the field operator, and performed by Boskalis Subsea Services, the diving contractor, onboard the diving support vessel Boka Atlantis. The objective was twofold: document the level of diving stress during saturation operations in the Danish sector, and compare the performances of two saturation procedures, the Boskalis and the NORSOK procedures. MATERIALS AND METHODS: Fourteen divers volunteered for the study. The monitoring package include weight and temperature measurements, psychomotor tests (objective evaluation) and questionnaires (subjective evaluation), Doppler bubble detection and bioimpedance. The results were presented in a radar diagram that provides a general view of the situation. RESULTS: The data were analysed along 3 dimensions: work and environmental, desaturation bubbles, oxidative stress and inflammation. The results showed little or no variations from the reference values. No bubbles were detected after excursion dives and the final decompression, except for two divers with a grade 1 after arriving at surface. No statistical difference could be found between the Boskalis and the NORSOK saturation procedures. CONCLUSIONS: At a depth of 40-50 msw corresponding to the Danish sector, the two saturation procedures monitored induce no or little stress to the divers. The divers know how to manage their diet, equilibrate their hydration and pace their effort. Data available on divers' post saturation period show a recovery over the 24-48 hours following the end of the decompression. Further research should focus on diving deeper than 100 msw where a greater stress can be anticipated.

Innovative health tracker that provides advanced functionalities to support and guide users in modifying their lifestyle: a Straussian ground theory approach.

Objective: Health can be described as the state of homeostasis and optimal functioning across various bio-psycho-social dimensions and processes, allowing an individual to adapt and respond effectively to extrinsic and intrinsic challenges. Our thoughts, choices, behaviors, experiences, and feelings shape our existence. By transitioning from unconscious reactions to conscious responses, we can establish novel habits and behaviors, actively embracing positive shifts in our lifestyle. Subjects and methods: The presented examination focuses on the smartwatch (SW), analyzing the incorporation of potentially progressive attributes that could enrich our lifestyle pursuits. The objective is not the health disorders themselves but the employment of wearable devices to create a strong sense of coherence in the Straussian grounded theory approach. The study had no subjects. Results: The potential of the SW has been partially explored in lifestyle intervention, modification, research, and practice. Conclusion: Based on our examination, creating an innovative SW capable of aiding individuals in better comprehending their behaviors and motivating them toward comprehensive changes in their lifestyle is a challenging yet attainable endeavor. Our ambition is to bring into existence SW capable of comprehensively measuring and evaluating interoception, circadian rhythm (CR), selected lifestyle pillars, and their associated components, and seamlessly integrating them into current SW features. It focuses on boosting motivation, maintenance, and amelioration regarding one's lifestyle. The novel approach strives to boost both immediate and underlying factors that actively contribute to improving one's metacognition.

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

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

Tough, self-healing, adhesive double network conductive hydrogel based on gelatin-polyacrylamide covalently bridged by oxidized sodium alginate for durable wearable sensors.

Pursuing high-performance conductive hydrogels is still hot topic in development of advanced flexible wearable devices. Herein, a tough, self-healing, adhesive double network (DN) conductive hydrogel (named as OSA-(Gelatin/PAM)-Ca, O-(G/P)-Ca) was prepared by bridging gelatin and polyacrylamide network with functionalized polysaccharide (oxidized sodium alginate, OSA) through Schiff base reaction. Thanks to the presence of multiple interactions (Schiff base bond, hydrogen bond, and metal coordination) within the network, the prepared hydrogel showed outstanding mechanical properties (tensile strain of 2800 % and stress of 630 kPa), high conductivity (0.72 S/m), repeatable adhesion performance and excellent self-healing ability (83.6 %/79.0 % of the original tensile strain/stress after self-healing). Moreover, the hydrogel-based sensor exhibited high strain sensitivity (GF = 3.66) and fast response time (<0.5 s), which can be used to monitor a wide range of human physiological signals. Based on this, excellent compression sensitivity (GF = 0.41 kPa-1 in the range of 90-120 kPa), a three-dimensional (3D) array of flexible sensor was designed to monitor the intensity of pressure and spatial force distribution. In addition, a gel-based wearable sensor was accurately classified and recognized ten types of gestures, achieving an accuracy rate of >96.33 % both before and after self-healing under three machine learning models (the decision tree, SVM, and KNN). This paper provides a simple method to prepare tough and self-healing conductive hydrogel as flexible multifunctional sensor devices for versatile applications in fields such as healthcare monitoring, human-computer interaction, and artificial intelligence.

Tiny-Machine-Learning-Based Supply Canal Surface Condition Monitoring.

The South-to-North Water Diversion Project in China is an extensive inter-basin water transfer project, for which ensuring the safe operation and maintenance of infrastructure poses a fundamental challenge. In this context, structural health monitoring is crucial for the safe and efficient operation of hydraulic infrastructure. Currently, most health monitoring systems for hydraulic infrastructure rely on commercial software or algorithms that only run on desktop computers. This study developed for the first time a lightweight convolutional neural network (CNN) model specifically for early detection of structural damage in water supply canals and deployed it as a tiny machine learning (TinyML) application on a low-power microcontroller unit (MCU). The model uses damage images of the supply canals that we collected as input and the damage types as output. With data augmentation techniques to enhance the training dataset, the deployed model is only 7.57 KB in size and demonstrates an accuracy of 94.17 ± 1.67% and a precision of 94.47 ± 1.46%, outperforming other commonly used CNN models in terms of performance and energy efficiency. Moreover, each inference consumes only 5610.18 µJ of energy, allowing a standard 225 mAh button cell to run continuously for nearly 11 years and perform approximately 4,945,055 inferences. This research not only confirms the feasibility of deploying real-time supply canal surface condition monitoring on low-power, resource-constrained devices but also provides practical technical solutions for improving infrastructure security.

PMUT-Based System for Continuous Monitoring of Bolted Joints Preload.

In this paper, we present a bolt preload monitoring system, including the system architecture and algorithms. We show how Finite Element Method (FEM) simulations aided the design and how we processed signals to achieve experimental validation. The preload is measured using a Piezoelectric Micromachined Ultrasonic Transducer (PMUT) in pulse-echo mode, by detecting the Change in Time-of-Flight (CTOF) of the acoustic wave generated by the PMUT, between no-load and load conditions. We performed FEM simulations to analyze the wave propagation inside the bolt and understand the effect of different configurations and parameters, such as transducer bandwidth, transducer position (head/tip), presence or absence of threads, as well as the frequency of the acoustic waves. In order to couple the PMUT to the bolt, a novel assembly process involving the deposition of an elastomeric acoustic impedance matching layer was developed. We achieved, for the first time with PMUTs, an experimental measure of bolt preload from the CTOF, with a good signal-to-noise ratio. Due to its low cost and small size, this system has great potential for use in the field for continuous monitoring throughout the operative life of the bolt.

Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.

Repurposing Sewage and Toilet Systems: Environmental, Public Health, and Person-Centered Healthcare Applications.

Global terrestrial water supplies are rapidly depleting due to the consequences of climate change. Water scarcity results in an inevitable compromise of safe hygiene and sanitation practices, leading to the transmission of water-borne infectious diseases, and the preventable deaths of over 800.000 people each year. Moreover, almost 500 million people lack access to toilets and sanitation systems. Ecosystems are estimated to be contaminated by 6.2 million tons of nitrogenous products from human wastewater management practices. It is therefore imperative to transform toilet and sewage systems to promote equitable access to water and sanitation, improve public health, conserve water, and protect ecosystems. Here, the integration of emerging technologies in toilet and sewage networks to repurpose toilet and wastewater systems is reviewed. Potential applications of these systems to develop sustainable solutions to environmental challenges, promote public health, and advance person-centered healthcare are discussed.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Advanced biomechanical analytics: Wearable technologies for precision health monitoring in sports performance.

Objective: This study investigated the impact of wearable technologies, particularly advanced biomechanical analytics and machine learning, on sports performance monitoring and intervention strategies within the realm of physiotherapy. The primary aims were to evaluate key performance metrics, individual athlete variations and the efficacy of machine learning-driven adaptive interventions. Methods: The research employed an observational cross-sectional design, focusing on the collection and analysis of real-world biomechanical data from athletes engaged in sports physiotherapy. A representative sample of athletes from Bahawalpur participated, utilizing Dring Stadium as the primary data collection venue. Wearable devices, including inertial sensors (MPU6050, MPU9250), electromyography (EMG) sensors (MyoWare Muscle Sensor), pressure sensors (FlexiForce sensor) and haptic feedback sensors, were strategically chosen for their ability to capture diverse biomechanical parameters. Results: Key performance metrics, such as heart rate (mean: 76.5 bpm, SD: 3.2, min: 72, max: 80), joint angles (mean: 112.3 degrees, SD: 6.8, min: 105, max: 120), muscle activation (mean: 43.2%, SD: 4.5, min: 38, max: 48) and stress and strain features (mean: [112.3 ], SD: [6.5 ]), were analyzed and presented in summary tables. Individual athlete analyses highlighted variations in performance metrics, emphasizing the need for personalized monitoring and intervention strategies. The impact of wearable technologies on athletic performance was quantified through a comparison of metrics recorded with and without sensors. Results consistently demonstrated improvements in monitored parameters, affirming the significance of wearable technologies. Conclusions: The study suggests that wearable technologies, when combined with advanced biomechanical analytics and machine learning, can enhance athletic performance in sports physiotherapy. Real-time monitoring allows for precise intervention adjustments, demonstrating the potential of machine learning-driven adaptive interventions.

Monitoring Fatigue Damage of Orthotropic Steel Decks Using Nonlinear Ultrasonic Waves.

Orthotropic steel decks (OSDs) are commonly used in the construction of bridges due to their load-bearing capabilities. However, they are prone to fatigue damage over time due to the cyclic loads from vehicles. Therefore, the early structural health monitoring of fatigue damage in OSDs is crucial for ensuring bridge safety. Moreover, Lamb waves, as elastic waves propagating in OSD plate-like structures, are characterized by their long propagation distances and minimal attenuation. This paper introduces a method of emitting high-energy ultrasonic waves onto the OSD surface to capture the nonlinear Lamb waves formed, thereby calculating the nonlinear parameters. These parameters are then correlated with the fatigue damage endured, forming a damage index (DI) for monitoring the fatigue life of OSDs. Experimental results indicate that as fatigue damage increases, the nonlinear parameters exhibit a significant initial increase followed by a decrease. The behavior is distinct from the characteristic parameters of linear ultrasound (velocity and energy), which also exhibit changes but to a relatively smaller extent. The proposed DI and fatigue life based on nonlinear parameters can be fitted with a Gaussian curve, with the R-squared value of the fitting curve being close to 1. Additionally, this paper discusses the influence of rib welds within the OSDs on the DI, whereby as fatigue damage increases, it enlarges the value of the nonlinear parameters without altering their trend. The proposed method provides a more effective approach for monitoring early fatigue damage in OSDs.

Enhancement of the Electric-Force Response of Carbon Black/Silicone Rubber Composites by Silane Coupling Agents.

Flexible strain sensors have a wide range of applications in the field of health monitoring of seismic isolation bearings. However, the nonmonotonic response with shoulder peaks limits their application in practical engineering. Here we eliminate the shoulder peak phenomenon during the resistive-strain response by adjusting the dispersion of conductive nanofillers. In this paper, carbon black (CB)/methyl vinyl silicone rubber (VMQ) composites were modified by adding a silane coupling agent (KH550). The results show that the addition of KH550 eliminates the shoulder peak phenomenon in the resistive response signal of the composites. The reason for the disappearance of the shoulder peak phenomenon was explained, and at the same time, the mechanical properties of the composites were enhanced, the percolation threshold was reduced, and they had excellent strain-sensing properties. It also exhibited excellent stability and repeatability during 18,000 cycles of loading-unloading. The resistance-strain response mechanism was explained by the tunneling effect theoretical model analysis. It was shown that the sensor has a promising application in the health monitoring of seismic isolation bearings.

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process.

In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoder, UnSupervised Anomaly Detection on Multivariate Time Series (USAD), and Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (TranAD), showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.

Application of non-contact sensors for health monitoring in hospitals: a narrative review.

The continuous monitoring of the health status of patients is essential for the effective monitoring of disease progression and the management of symptoms. Recently, health monitoring using non-contact sensors has gained interest. Therefore, this study aimed to investigate the use of non-contact sensors for health monitoring in hospital settings and evaluate their potential clinical applications. A comprehensive literature search was conducted using PubMed to identify relevant studies published up to February 26, 2024. The search terms included "hospital," "monitoring," "sensor," and "non-contact." Studies that used non-contact sensors to monitor health status in hospital settings were included in this review. Of the 38 search results, five studies met the inclusion criteria. The non-contact sensors described in the studies were radar, infrared, and microwave sensors. These non-contact sensors were used to obtain vital signs, such as respiratory rate, heart rate, and body temperature, and were then compared with the results from conventional measurement methods (polysomnography, nursing records, and electrocardiography). In all the included studies, non-contact sensors demonstrated a performance similar to that of conventional health-related parameter measurement methods. Non-contact sensors are expected to be a promising solution for health monitoring in hospital settings.

A robust self-supervised approach for fine-grained crack detection in concrete structures.

This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies, incorporating object detection and image segmentation techniques. Recent efforts have integrated complex techniques such as deep convolutional neural networks (DCNNs) and transformers for this task. However, these techniques encounter challenges in localizing fine-grained cracks. This paper presents a self-supervised 'you only look once' (SS-YOLO) approach that utilizes a YOLOv8 model. The novel methodology amalgamates different attention approaches and pseudo-labeling techniques, effectively addressing challenges in fine-grained crack detection and segmentation in concrete structures. It utilizes convolution block attention (CBAM) and Gaussian adaptive weight distribution multi-head self-attention (GAWD-MHSA) modules to accurately identify and segment fine-grained cracks in concrete buildings. Additionally, the assimilation of curriculum learning-based self-supervised pseudo-labeling (CL-SSPL) enhances the model's ability when applied to limited-size data. The efficacy and viability of the proposed approach are demonstrated through experimentation, results, and ablation analysis. Experimental results indicate a mean average precision (mAP) of at least 90.01%, an F1 score of 87%, and an intersection over union threshold greater than 85%. It is evident from the results that the proposed method yielded at least 2.62% and 4.40% improvement in mAP and F1 values, respectively, when tested on three diverse datasets. Moreover, the inference time taken per image is 2 ms less than that of the compared methods.

Urine multi-index intelligent detection based on polymer/paper hybrid microfluidic biochip for hyperuricemia monitoring.

Hyperuricemia (HUA) has gradually become a public health burden as an independent risk factor for a variety of chronic diseases. Herein, a user-friendly point-of-care (POC) detection system (namely "Smart-HUA-Monitor") based on smartphone-assisted paper-based microfluidic is proposed for colorimetric quantification of HUA urinary markers, including uric acid (UA), creatinine (CR) and pH. The detection limits of UA and CR were 0.0178 and 0.5983 mM, respectively, and the sensitivity of pH were 0.1. The method was successfully validated in artificial urine samples and 100 clinical samples. Bland-Altman plots showed a high consistency between µPAD and the testing instruments (HITACHI 7600 Automatic Analyzer, URIT-500B Urine Analyzer and AU5800B automatic biochemical analyzer) in hospital. Smart-HUA-Monitor provides an accurate quantitative, rapid, low-cost and reliable tool for the monitoring and early diagnosis of HUA urine indicators.

A reversible implantable memristor for health monitoring applications.

Conventional implantable electronics based on von Neumann architectures encounter significant limitations in computing and processing vast biological information due to computational bottlenecks. The memristor with integrated memory-computing and low power consumption offer a promising solution to overcome the computational bottleneck and Moore's law limitations of traditional silicon-based implantable devices, making them the most promising candidates for next-generation implantable devices. In this work, a highly stable memristor with an Ag/BaTiO3/MnO2/FTO structure was fabricated, demonstrating retention characteristics exceeding 1200 cycles and endurance above 1000 s. The device successfully exhibited three-stage responses to biological signals after implantation in SD (Sprague-Dawley) rats. Importantly, the memristor perform remarkable reversibility, maintaining over 100 cycles of stable repetition even after extraction from the rat. This study provides a new perspective on the biomedical application of memristors, expanding the potential of implantable memristive devices in intelligent medical fields such as health monitoring and auxiliary diagnostics.