Review:Wearable Sweat Sensors for Diseases Prevention and Monitoring

Advanced in wireless technologies and flexible materials with great biocompatibility,wearable devices have been utilized in the field of healthcare,sports management,and diseases prevention,which have been widely applied in current electronic equipment.Sweat,as a common metabolite on the skin surface,contains a wealth of biomarkers for disease detection and diagnosis.Therefore,developing wearable sweat sensors can provide a non⁃invasive method for health data collecting,sports monitoring,and clinical diagnosis in a convenient way.Recent research in sweat metabolomics has offered a lot of information for sweat analysis and the wearable sweat sensors with small size,various sensing,and transmission units,and good skin contact has exhibited dynamic multi⁃signal detection.This article introduces the biomarkers in sweat related to different diseases and the current development of sweat sensors for user􀆳s activation monitoring and diseases detection.The barriers and difficulties in the future are also discussed and perspectives in the next generation sweat sensors are proposed.

Intestinal ultrasound as a non-invasive tool to monitor inflammatory bowel disease activity and guide clinical decision making

Intestinal ultrasound(IUS)is a non-invasive,real-time,cross-sectional imaging tool that can be used at the point-of-care to assess disease activity in patients with Crohn’s disease or ulcerative colitis.IUS promotes quick and impactful treatment decisions that can modify disease progression and enhance patient compliance.This review will summarize the technical aspects of IUS,the evidence to support the use of IUS in disease activity monitoring,the comparison of IUS to current standard of care monitoring modalities such as colonoscopy and calprotectin,and the optimal positioning of IUS in a tight-control monitoring strategy.

A Preliminary Study of C-reactive Protein in the Diagnosis and Monitoring of Lyme Disease

Concentrations of C-reactive protein (CRP) in the serum of 14 patients suffering from Lyme diseasc were measured. 86% of these patients were found to have abnormally high concentrations of serum CRP (range 14-158 mg/L). The CRP concentration of a 60-yearold patient abated from 29 mg/L to 13 mg/L after treatrnent. Results suggest that serum CRP concentration can provide a valuable and accurate means for the clinical diagnosis and monitoring of Lyme disease

Monitoring Thosea sinensis Walker in Tea Plantations Based on UAV Multi- Spectral Image

Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community.Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests.In this work,based on the unmanned aerial vehicle(UAV)platform,five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations.By combining the minimum redundancy maximum relevance(mRMR)with the selected spectral features,a comprehensive spectral selection strategy was proposed.Then,based on the selected spectral features,three classic machine learning algorithms,including random forest(RF),support vector machine(SVM),and k-nearest neighbors(KNN)were used to construct the pest monitoring model and were evaluated and compared.The results showed that the strategy proposed in this work obtained ideal monitoring accuracy by only using the combination of a few optimized features(2 or 4).In order to differentiate the healthy and TSW-damaged areas(2-class model),the monitoring accuracies of all the three models were computed,which were above 96%.The RF model used the least number of features,including only SAVI and Bandred.In order to further discriminate the pest incidence levels(3-class model),the monitoring accuracies of all the three models were computed,which were above 80%,among which the RF algorithm based on SAVI,Band_(red),VARI__(green),and Band_(red_edge) features achieve the highest accuracy(OAA of 87%,and Kappa of 0.79).Considering the computational cost and model accuracy,this work recommends the RF model based on a few optimal feature combinations to monitor and distinguish the severity of TSW in tea plantations.According to the UAV remote sensing mapping results,the TSW infestation exhibited an aggregated distribution pattern.The spatial information of occurrence and severity can offer effective guidance for precise control of the pest.In addition,the relevant methods provide a reference for monitoring other leaf-eating pests,effectively improving the management level of plant protection in tea plantations,and guaranting the yield and quality of tea plantations.

Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems

As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae pv.oryzae), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was formed.The capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple sensors.Meanwhile, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale.

Diagnosis of gastroesophageal reflux disease in elderly subjects using 24-houres ophageal pH monitoring

Objective To evaluate the relationship between the parameters of 24 hour esophageal pH monitoring and gastroesophageal reflux disease (GERD) among elderly subjects. Methods Twenty four hour esophageal pH monitoring was carried out in 20 elderly subjects without apparent GERD symptoms (controls) and 69 suspected GERD subjects.Results Normal values of the parameters from 20 elderly controls were obtained. Percent of total time, percent of supine time and percent of upright time in which the pH was <4 (indicating reflux) were less than 3.3%, 1.4%, 5.5%, respectively. The number of reflux episodes and episodes lasting longer than 5 minutes were less than 65 and 2 times respectively. The values obtained in 66 GERD suspected subjects were significantly different from those in norrmal controls. The differences of reflux parameters between the esophagitis group and non esophagitis group, such as percent of total time with pH<4, percent of supine time with pH<4 and number of reflux lasting longer than 5 minutes were also significant. Conclusions About 51.6% patients (34/66) with reflux symptoms but without endoscopic evidence of esophagitis were definitely diagnosed as GERD by esophageal pH monitoring. Duration of esophageal acid exposure correlated with the severity of GERD.