From motion to emotion: exploring challenging behaviors in autism spectrum disorder through analysis of wearable physiology and movement.

Objective: This study aims to evaluate the efficacy of wearable physiology and movement sensors in identifying a spectrum of challenging behaviors, including self-injurious behavior (SIB), in children and teenagers with autism spectrum disorder (ASD) in real-world settings.

Approach: We utilized a long-short-term memory (LSTM) network with features derived using the wavelet scatter transform to analyze physiological biosignals, including electrodermal activity and skin temperature, alongside three-dimensional movement data captured via accelerometers. The study was conducted in naturalistic environments, focusing on participants' daily activities.

Optimization and analytical validation of the Allplex HPV28 genotyping assay for use in first-void urine samples.

Despite first-void urine (FVU) being increasingly recognized as a credible specimen for human papillomavirus (HPV) detection, there is a lack of well-validated testing methods providing full quantitative genotyping required for vaccine impact monitoring from FVU samples. The Allplex HPV28 assay, capable of individually detecting 28 HPV genotypes, presents a promising method. We aimed to evaluate its genotype-specific performance on FVU samples, following optimization of FVU preanalytics.

Peritraumatic C-reactive protein levels predict pain outcomes following traumatic stress exposure in a sex-dependent manner.

Background: Chronic pain following traumatic stress exposure (TSE) is common. Increasing evidence suggests inflammatory/immune mechanisms are induced by TSE, play a key role in the recovery process versus development of post-TSE chronic pain, and are sex specific. In this study, we tested the hypothesis that the inflammatory marker C-reactive protein (CRP) is associated with chronic pain after TSE in a sex-specific manner.

Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second Trimesters.

Hypertensive disorders of pregnancy (HDPs) remain a major challenge in maternal health. Early prediction of HDPs is crucial for timely intervention. Most existing predictive machine learning (ML) models rely on costly methods like blood, urine, genetic tests, and ultrasound, often extracting features from data gathered throughout pregnancy, delaying intervention.