Reporting of pre-existing multiple long-term conditions in physical rehabilitation for long COVID: a scoping review.

Background: Physical rehabilitation may improve health and wellbeing outcomes for some adults living with long COVID. However, individuals living with pre-existing multiple long-term conditions (MLTCs) and long COVID may have additional rehabilitation challenges. This scoping review aims to identify the available evidence describing physical rehabilitation interventions for adults living with long COVID, to systematically map the reporting of pre-existing MLTCs, and to describe the characteristics of physical rehabilitation interventions used in adults with both pre-existing long-term conditions (LTCs) and long COVID.

AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.

Machine learning (ML) methods offer opportunities for gaining insights into the intricate workings of complex biological systems, and their applications are increasingly prominent in the analysis of omics data to facilitate tasks, such as the identification of novel biomarkers and predictive modeling of phenotypes. For scientists and domain experts, leveraging user-friendly ML pipelines can be incredibly valuable, enabling them to run sophisticated, robust, and interpretable models without requiring in-depth expertise in coding or algorithmic optimization. By streamlining the process of model development and training, researchers can devote their time and energies to the critical tasks of biological interpretation and validation, thereby maximizing the scientific impact of ML-driven insights.

DGCNN approach links metagenome-derived taxon and functional information providing insight into global soil organic carbon.

Metagenomics can provide insight into the microbial taxa present in a sample and, through gene identification, the functional potential of the community. However, taxonomic and functional information are typically considered separately in downstream analyses. We develop interpretable machine learning (ML) approaches for modelling metagenomic data, combining the biological representation of species with their associated genetically encoded functions within models.

Network Analysis of Women in Otolaryngology and Demographic Trends.

Objective: This study analyzes gender disparities between men and women otolaryngology faculty in the top 20 otolaryngology departments ranked by research output and discusses the implications of these disparities.

Methods: This was a cross-sectional study of all articles published by faculty from January 2020 to December 2021 at the top 20 otolaryngology departments as ranked by Doximity's 2022 research output report. Integrated data from Web of Science, faculty directories, and NIH RePORT were used to collect data on faculty.