Investigating circadian patterns in electrochemical skin Conductance: A year-long retrospective analysis using home-based devices.

Objective: This study presents a comprehensive analysis of electrochemical skin conductance (ESC) values collected from over 1.9 million measurements using Withings Body Scan and Body Comp scales. It aimed at establishing descriptive values for ESC while correcting for the influence of age, sex, and circadian and seasonal variations on sudomotor function.

Changes in Physical Activity, Heart Rate, and Sleep Measured by Activity Trackers During the COVID-19 Pandemic Across 34 Countries: Retrospective Analysis.

Background: The COVID-19 pandemic disrupted behavior within populations, affecting physical activity (PA), heart rate (HR), and sleep characteristics in particular. Activity trackers provide unique insights into these changes, enabling large-scale, real-time monitoring.

Objective: This study aims to analyze the associations between the features of the COVID-19 pandemic worldwide and PA, HR, and sleep parameters, using data collected from activity trackers over a 3-year period.

Electrochemical skin conductance: a tool for risk stratification and early anticipation of diabetic foot ulcers.

Introduction: Diabetic foot ulcers (DFUs) are a major complication of diabetes, leading to high morbidity, mortality, and healthcare costs. Current DFU risk stratification relies on clinical examination, which can be subjective. Electrochemical Skin Conductance (ESC), measured via Sudoscan, offers an objective assessment of small fiber dysfunction.

Circadian-like patterns in electrochemical skin conductance measured from home-based devices: a retrospective study.

In this study, we investigated the potential of electrochemical skin conductance (ESC) measurements gathered from home-based devices to detect circadian-like patterns. We analyzed data from 43,284 individuals using the Withings Body Comp or Body Scan scales, which provide ESC measurements. Our results highlighted a circadian pattern of ESC values across different age groups and countries.

Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer.

Determining which treatment to provide to men with prostate cancer (PCa) is a major challenge for clinicians. Currently, the clinical risk-stratification for PCa is based on clinico-pathological variables such as Gleason grade, stage and prostate specific antigen (PSA) levels. But transcriptomic data have the potential to enable the development of more precise approaches to predict evolution of the disease.

Immune-focused multi-omics analysis of prostate cancer: leukocyte Ig-Like receptors are associated with disease progression.

Prostate cancer (PCa) immunotherapy has shown limited efficacy so far, even in advanced-stage cancers. The success rate of PCa immunotherapy might be improved by approaches more adapted to the immunobiology of the disease. The objective of this study was to perform a multi-omics analysis to identify immune genes associated with PCa progression to better characterize PCa immunobiology and propose new immunotherapeutic targets.

Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data.

The identification of biomarker signatures in omics molecular profiling is usually performed to predict outcomes in a precision medicine context, such as patient disease susceptibility, diagnosis, prognosis, and treatment response. To identify these signatures, we have developed a biomarker discovery tool, called BioDiscML. From a collection of samples and their associated characteristics, i.