Lesional skin of seborrheic dermatitis patients is characterized by skin barrier dysfunction and correlating alterations in the stratum corneum ceramide composition.

Seborrheic dermatitis (SD) is a chronic inflammatory skin disease characterized by erythematous papulosquamous lesions in sebum rich areas such as the face and scalp. Its pathogenesis appears multifactorial with a disbalanced immune system, Malassezia driven microbial involvement and skin barrier perturbations. Microbial involvement has been well described in SD, but skin barrier involvement remains to be properly elucidated.

A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity.

Drug development for mood disorders can greatly benefit from the development of robust, reliable, and objective biomarkers. The incorporation of smartphones and wearable devices in clinical trials provide a unique opportunity to monitor behavior in a non-invasive manner. The objective of this study is to identify the correlations between remotely monitored self-reported assessments and objectively measured activities with depression severity assessments often applied in clinical trials.

Treatment Detection and Movement Disorder Society-Unified Parkinson's Disease Rating Scale, Part III Estimation Using Finger Tapping Tasks.

The validation of objective and easy-to-implement biomarkers that can monitor the effects of fast-acting drugs among Parkinson's disease (PD) patients would benefit antiparkinsonian drug development. We developed composite biomarkers to detect levodopa/carbidopa effects and to estimate PD symptom severity. For this development, we trained machine learning algorithms to select the optimal combination of finger tapping task features to predict treatment effects and disease severity.

Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.

Background: Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity.

A multimodal, comprehensive characterization of a cutaneous wound model in healthy volunteers.

Development of pharmacological interventions for wound treatment is challenging due to both poorly understood wound healing mechanisms and heterogeneous patient populations. A standardized and well-characterized wound healing model in healthy volunteers is needed to aid in-depth pharmacodynamic and efficacy assessments of novel compounds. The current study aims to objectively and comprehensively characterize skin punch biopsy-induced wounds in healthy volunteers with an integrated, multimodal test battery.

Smartphone and Wearable Sensors for the Estimation of Facioscapulohumeral Muscular Dystrophy Disease Severity: Cross-sectional Study.

Background: Facioscapulohumeral muscular dystrophy (FSHD) is a progressive neuromuscular disease. Its slow and variable progression makes the development of new treatments highly dependent on validated biomarkers that can quantify disease progression and response to drug interventions.

Objective: We aimed to build a tool that estimates FSHD clinical severity based on behavioral features captured using smartphone and remote sensor data.

Objective Monitoring of Facioscapulohumeral Dystrophy During Clinical Trials Using a Smartphone App and Wearables: Observational Study.

Background: Facioscapulohumeral dystrophy (FSHD) is a progressive muscle dystrophy disorder leading to significant disability. Currently, FSHD symptom severity is assessed by clinical assessments such as the FSHD clinical score and the Timed Up-and-Go test. These assessments are limited in their ability to capture changes continuously and the full impact of the disease on patients' quality of life.

Development and technical validation of a smartphone-based pediatric cough detection algorithm.

Introduction: Coughing is a common symptom in pediatric lung disease and cough frequency has been shown to be correlated to disease activity in several conditions. Automated cough detection could provide a noninvasive digital biomarker for pediatric clinical trials or care. The aim of this study was to develop a smartphone-based algorithm that objectively and automatically counts cough sounds of children.

Impacts of changes in alcohol consumption patterns during the first 2020 COVID-19 restrictions for people with and without mental health and neurodevelopmental conditions: A cross sectional study in 13 countries.

Background: The initial period of COVID-19-related restrictions affected substance use in some population groups. We explored how changes in alcohol use at the beginning of the pandemic impacted the health and wellbeing of people with and without mental health and neurodevelopmental conditions (MHDCs).

Methods: Data came from the Global Drug Survey Special Edition on COVID-19 conducted in May-June 2020.

A cross-sectional study in healthy elderly subjects aimed at development of an algorithm to increase identification of Alzheimer pathology for the purpose of clinical trial participation.

Background: In the current study, we aimed to develop an algorithm based on biomarkers obtained through non- or minimally invasive procedures to identify healthy elderly subjects who have an increased risk of abnormal cerebrospinal fluid (CSF) amyloid beta42 (Aβ) levels consistent with the presence of Alzheimer's disease (AD) pathology. The use of the algorithm may help to identify subjects with preclinical AD who are eligible for potential participation in trials with disease modifying compounds being developed for AD. Due to this pre-selection, fewer lumbar punctures will be needed, decreasing overall burden for study subjects and costs.

Postdischarge Recovery after Acute Pediatric Lung Disease Can Be Quantified with Digital Biomarkers.

Background: Pediatric patients admitted for acute lung disease are treated and monitored in the hospital, after which full recovery is achieved at home. Many studies report in-hospital recovery, but little is known regarding the time to full recovery after hospital discharge. Technological innovations have led to increased interest in home-monitoring and digital biomarkers.

Development and Technical Validation of a Smartphone-Based Cry Detection Algorithm.

The duration and frequency of crying of an infant can be indicative of its health. Manual tracking and labeling of crying is laborious, subjective, and sometimes inaccurate. The aim of this study was to develop and technically validate a smartphone-based algorithm able to automatically detect crying.

Technical validity and usability of a novel smartphone-connected spirometry device for pediatric patients with asthma and cystic fibrosis.

Background: Diagnosis and follow-up of respiratory diseases traditionally rely on pulmonary function tests (PFTs), which are currently performed in hospitals and require trained personnel. Smartphone-connected spirometers, like the Air Next spirometer, have been developed to aid in the home monitoring of patients with pulmonary disease. The aim of this study was to investigate the technical validity and usability of the Air Next spirometer in pediatric patients.