An explainable analysis of diabetes mellitus using statistical and artificial intelligence techniques.

Background: Diabetes mellitus (DM) is a chronic disease prevalent worldwide, requiring a multifaceted analytical approach to improve early detection and subsequent mitigation of morbidity and mortality rates. This research aimed to develop an explainable analysis of DM by combining sociodemographic and clinical data with statistical and artificial intelligence (AI) techniques.

Methods: Leveraging a small dataset that includes sociodemographic and clinical profiles of diabetic and non-diabetic individuals, we employed a diverse set of statistical and AI models for predictive purposes and assessment of DM risk factors.

Prevalence of Tobacco and Poly-Tobacco Use as a Function of Sexual Orientation Identity and Sex.

Introduction: Limited research has characterized poly-tobacco use (i.e., use of at least two nicotine/tobacco products) among adults with sexual minoritized (SM) identities (i.

Effect of dog-owner interaction on post-operative pain perception and stress of dogs and variability in their behavioural patterns.

Pain, a multifaceted condition associated with actual or potential tissue damage, transcends nociception and is characterised as a subjective, sensory, and emotional experience. Extensive literature describing the adverse effects of untreated post-surgical pain emphasises the necessity of a comprehensive pain management protocol, incorporating both pharmacological and non-pharmacological strategies to ensure successful patient outcomes. The present study aimed to determine whether a positive dog-owner interaction influences post-operative pain perception and stress (POPPS), as well as behavioural inactive rate variability in bitches that underwent elective surgery.

Modification of Quark-Gluon Distributions in Nuclei by Correlated Nucleon Pairs.

We extend the QCD Parton Model analysis using a factorized nuclear structure model incorporating individual nucleons and pairs of correlated nucleons. Our analysis of high-energy data from lepton deep-inelastic scattering, Drell-Yan, and W and Z boson production simultaneously extracts the universal effective distribution of quarks and gluons inside correlated nucleon pairs, and their nucleus-specific fractions. Such successful extraction of these universal distributions marks a significant advance in our understanding of nuclear structure properties connecting nucleon- and parton-level quantities.