The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review.

Containing the COVID-19 pandemic requires rapidly identifying infected individuals. Subtle changes in physiological parameters (such as heart rate, respiratory rate, and skin temperature), discernible by wearable devices, could act as early digital biomarkers of infections. Our primary objective was to assess the performance of statistical and algorithmic models using data from wearable devices to detect deviations compatible with a SARS-CoV-2 infection. We searched MEDLINE, Embase, Web of Science, the Cochrane Central Register of Controlled Trials (known as CENTRAL), International Clinical Trials Registry Platform, and ClinicalTrials.gov on July 27, 2021 for publications, preprints, and study protocols describing the use of wearable devices to identify a SARS-CoV-2 infection. Of 3196 records identified and screened, 12 articles and 12 study protocols were analysed. Most included articles had a moderate risk of bias, as per the National Institute of Health Quality Assessment Tool for Observational and Cross-Sectional Studies. The accuracy of algorithmic models to detect SARS-CoV-2 infection varied greatly (area under the curve 0·52-0·92). An algorithm's ability to detect presymptomatic infection varied greatly (from 20% to 88% of cases), from 14 days to 1 day before symptom onset. Increased heart rate was most frequently associated with SARS-CoV-2 infection, along with increased skin temperature and respiratory rate. All 12 protocols described prospective studies that had yet to be completed or to publish their results, including two randomised controlled trials. The evidence surrounding wearable devices in the early detection of SARS-CoV-2 infection is still in an early stage, with a limited overall number of studies identified. However, these studies show promise for the early detection of SARS-CoV-2 infection. Large prospective, and preferably controlled, studies recruiting and retaining larger and more diverse populations are needed to provide further evidence.

Top soil physical and chemical properties in Kazakhstan across a north-south gradient.

Kazakhstan's soil properties have yet to be comprehensively characterized. We sampled 40 sites consisting of ten major soil types at spring (wet) and late-summer (dry) seasons. The sample locations range from semi-arid to arid with an annual mean air temperature from 1.2 to 10.7 °C and annual precipitation from less than 200 to around 400 mm. Overall topsoil total (STC), organic (SOC), and inorganic (SIC) carbon did not change significantly between spring and late summer. STC and SOC show a wave like pattern from north to south with two maxima in northern and southern Kazakhstan and one minimum in central Kazakhstan. With a few exceptions SIC content at northern sites is generally low, whereas at Lake Balkhash SIC can exceed 75% of STC. Independent of the seasons, SOC significantly differed among soil types. Total nitrogen content distribution among our sampling sites followed a similar pattern as SOC with significant differences between soil types occurring in northern, central and southern Kazakhstan.