FreshRSS

🔒
❌ Acerca de FreshRSS
Hay nuevos artículos disponibles. Pincha para refrescar la página.
AnteayerInterdisciplinares

Assessing the impact of binge drinking and a prebiotic intervention on the gut-brain axis in young adults: protocol for a randomised controlled trial

Por: Prata-Martins · D. · Nobre · C. · Almeida-Antunes · N. · Azevedo · P. · Sousa · S. S. · Crego · A. · Cryan · J. · Sampaio · A. · Carbia · C. · Lopez-Caneda · E.
Introduction

Adolescence and youth are periods of significant maturational changes, which seem to involve greater susceptibility to disruptive events in the brain, such as binge drinking (BD). This pattern—characterised by repeated episodes of alcohol intoxication—is of particular concern, as it has been associated with significant alterations in the developing brain. Recent evidence indicates that alcohol may also induce changes in gut microbiota composition and that such disturbances can lead to impairments in both brain function and behaviour. Moreover, there is evidence suggesting that microbiota-targeted interventions (psychobiotics) may help mitigate alcohol-induced damage in individuals with chronic alcohol use, positively influencing cognitive and brain functioning. However, the triadic relationship between BD, gut microbiota and brain structure/function, as well as the therapeutic potential of gut microbiota-targeted interventions in young binge drinkers, remains largely unexplored.

Methods and analysis

This double-blind, parallel, randomised controlled study aims to evaluate whether a BD pattern disrupts gut microbiota diversity in young college students (primary outcome). Additionally, it seeks to determine whether alcohol-induced alterations in the microbial composition and function are associated with immunological, cognitive, neurostructural and neurofunctional impairments (secondary outcomes). A total of 82 college students (36 non/low drinkers and 46 binge drinkers (BDs)), matched for age and sex, will be recruited from the University of Minho (Portugal). During the pre-intervention phase, all participants will undergo a comprehensive assessment protocol, including gut microbiota profiling, measurement of inflammatory markers, neuropsychological testing and structural and functional MRI. BDs will then be randomly assigned to a 6-week intervention with either a prebiotic (inulin) or a placebo (maltodextrin). Post-intervention assessment will mirror the baseline protocol, and craving and alcohol use will be monitored for 3 months.

Ethics and dissemination

The present protocol was approved by the Ethics Committee for Social and Human Sciences of the University of Minho (CEICSH 078/2022), ensuring compliance with national and international ethical guidelines, including the Declaration of Helsinki. Participation is voluntary and preceded by informed consent, with confidentiality and data processing safeguarded in accordance with the General Data Protection Regulation. All procedures are safe and non-invasive, and the prebiotics used are recognised as food ingredients in Europe, hold Generally Recognized as Safe status in the USA and are classified as dietary fibres by the Food and Drug Administration. Findings will be disseminated in national and international scientific forums, with preference for publication in open-access, peer-reviewed journals.

Trial registration number

NCT05946083

Sex prediction through machine learning utilizing mandibular condyles, coronoid processes, and sigmoid notches features

by Isabela Bittencourt Basso, Pedro Felipe de Jesus Freitas, Aline Xavier Ferraz, Ana Julia Borkovski, Ana Laura Borkovski, Rosane Sampaio Santos, Rodrigo Nunes Rached, Erika Calvano Küchler, Angela Graciela Deliga Schroder, Cristiano Miranda de Araujo, Odilon Guariza-Filho

Characteristics of the mandible structures have been relevant in anthropological and forensic studies for sex prediction. This study aims to evaluate the coronoid process, condyle, and sigmoid notch patterns in sex prediction through supervised machine learning algorithms. Cephalometric radiographs from 410 dental records of patients were screened to investigate the morphology of the coronoid process, condyle, and sigmoid notch and the Co-Gn distance. The following machine learning algorithms were used to build the predictive models: Decision Tree, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Logistic Regression, Multilayer Perceptron Classifier, Random Forest Classifier, and Support Vector Machine (SVM). A 5-fold cross-validation approach was adopted to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were calculated for each model, and ROC curves were constructed. All tested variables demonstrated statistical significance (p
❌