Separate orexigenic hippocampal ensembles shape dietary choice by enhancing contextual memory and motivation.

The hippocampus (HPC) has emerged as a critical player in the control of food intake, beyond its well-known role in memory. While previous studies have primarily associated the HPC with food intake inhibition, recent research suggests a role in appetitive processes. Here we identified spatially distinct neuronal populations within the dorsal HPC (dHPC) that respond to either fats or sugars, potent natural reinforcers that contribute to obesity development.

Insecticidal effects of Sargassum vulgare and Caulerpa racemosa extracts on Aedes aegypti.

Dengue is a viral disease present in many regions of the world. Aedes aegypti transmits it, and the most effective way to eliminate the mosquito is during the larval stage. Seaweeds possess metabolites with insecticidal properties, making them potential sources of new larvicides and viable alternatives to synthetic products used to control insect vectors of diseases.

Reach and engagement on the history of nursing on social media in light of Pierre Lévy.

Objective: To analyze the reach and engagement on the history of nursing on social media of the Memory Center of the School of Nursing, Federal University of Minas Gerais (CEMENF/UFMG), in light of Pierre Lévy.

Methods: Documentary study carried out on CEMENF's Instagram and on the YouTube of the School of Nursing of UFMG, from September to December 2021. The findings were analyzed according to Pierre Lévy's concepts.

Association between nursing workload and staff size with the occurrence of adverse events and deaths of patients with COVID-19: A retrospective cohort study.

Background: The safety of patients in the intensive care unit (ICU) is significantly impacted by inadequate staffing of nursing professionals to meet the demand for care of critically ill patients. COVID-19 patients increased nurses' workload, particularly in high-intensity care settings like the ICU.

Aim: To evaluate the association of workload and number of patients per nursing professional with the occurrence of adverse events and death in critically ill patients with COVID-19 in the ICU.

Machine learning prediction model for oral mucositis risk in head and neck radiotherapy: a preliminary study.

Purpose: Oral mucositis (OM) reflects a complex interplay of several risk factors. Machine learning (ML) is a promising frontier in science, capable of processing dense information. This study aims to assess the performance of ML in predicting OM risk in patients undergoing head and neck radiotherapy.