Predictors of cyberchondria during the COVID-19 pandemic: A supervised machine learning approach

in Journal of Behavioral Addictions (2022, August 03)

Background and aims: Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. This behavior has been considered ... [more ▼]

Background and aims: Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. This behavior has been considered an emerging public health issue, which may have been exacerbated by the COVID-19 pandemic. The present study aimed to investigate changes in the severity of cyberchondria during the pandemic and identify predictors of cyberchondria at this time. Method: Self-reported data on cyberchondria severity (before and during the pandemic), attachment style, impulsivity traits, somatic symptoms, COVID-19-related fears, health anxiety, and intolerance of uncertainty were collected from 725 participants using an online survey distributed in French-speaking European countries during the first wave of the COVID-19 pandemic. Results: COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and interference with functioning increased, whereas the reassurance facet of cyberchondria decreased. Using supervised machine learning regression analyses, the specific COVID-19-related fears and health anxiety emerged as the strongest predictors of cyberchondria-related distress and interference with functioning during the pandemic. Conclusions: These findings provide evidence about the impact of the COVID-19 pandemic on cyberchondria and identify factors that should be considered in efforts to prevent and manage cyberchondria at times of public health crises. In addition, the findings have implications for the conceptualization and future assessment of cyberchondria. [less ▲]

PredictProtein - Predicting Protein Structure and Function for 29 Years

in Nucleic Acids Research (2021)

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and ... [more ▼]

Since 1992 PredictProtein (https://predictprotein.org) is a one-stop online resource for protein sequence analysis with its main site hosted at the Luxembourg Centre for Systems Biomedicine (LCSB) and queried monthly by over 3,000 users in 2020. PredictProtein was the first Internet server for protein predictions. It pioneered combining evolutionary information and machine learning. Given a protein sequence as input, the server outputs multiple sequence alignments, predictions of protein structure in 1D and 2D (secondary structure, solvent accessibility, transmembrane segments, disordered regions, protein flexibility, and disulfide bridges) and predictions of protein function (functional effects of sequence variation or point mutations, Gene Ontology (GO) terms, subcellular localization, and protein-, RNA-, and DNA binding). PredictProtein's infrastructure has moved to the LCSB increasing throughput; the use of MMseqs2 sequence search reduced runtime five-fold (apparently without lowering performance of prediction methods); user interface elements improved usability, and new prediction methods were added. PredictProtein recently included predictions from deep learning embeddings (GO and secondary structure) and a method for the prediction of proteins and residues binding DNA, RNA, or other proteins. PredictProtein.org aspires to provide reliable predictions to computational and experimental biologists alike. All scripts and methods are freely available for offline execution in high-throughput settings. [less ▲]

PrediSi: prediction of signal peptides and their cleavage positions

in Nucleic Acids Research (2004)

Préface

in Moreau, Sébastien (Ed.) Les CFL en mouvement depuis 75 ans. Histoire des Chemins de fer luxembourgeois (2022)

Preface

in Mein, Georg; Pause, Johannes (Eds.) Self and Society in the Corona Crisis. Perspectives from the Humanities and Social Sciences (2021)

Preface

in Steffgen, Georges; Gollwitzer, Mario (Eds.) Emotions and aggressive Behavior (2007)

Preface

in Lausen, Berthold; Krolak-Schwerdt, Sabine; Böhmer, Matthias (Eds.) Data Science, Learning by Latent Structures, and Knowledge Discovery (2015)

Preface

in Hietala, Marjatta; Helminen, Martti; Lahtinen, Merja (Eds.) Helsinki. Helsingfors. Historic Town Atlas (2009)

Preface

in Budak, Neven (Ed.) Towns and Communication, vol. 1: Communication in Towns (2009)

Preface

in Electronic Notes in Theoretical Computer Science (2006), 150(3), 12