Failure of colonization following gut microbiota transfer exacerbates DSS-induced colitis.

To study the impact of differing specific pathogen-free gut microbiomes (GMs) on a murine model of inflammatory bowel disease, selected GMs were transferred using embryo transfer (ET), cross-fostering (CF), and co-housing (CH). Prior work showed that the GM transfer method and the microbial composition of donor and recipient GMs can influence microbial colonization and disease phenotypes in dextran sodium sulfate-induced colitis. When a low richness GM was transferred to a recipient with a high richness GM via CH, the donor GM failed to successfully colonize, and a more severe disease phenotype resulted when compared to ET or CF, where colonization was successful.

Coming in hot: a qualitative investigation into perceptions of parents and doctors of reasons for the presentation of children with fever to the emergency department in England.

Introduction: Paediatric emergency department (ED) attendances and admissions in England are increasing. Fever is a common presenting problem for these attendances. Anxiety and misperceptions surrounding appropriate management of fever persist among parents.

Effect of two rhinoplasty techniques combined with vestibuloplasty on the cross-sectional area of the rostral nasal cavity in brachycephalic dogs with stenotic nares.

Objective: To compare preoperative and postoperative cross-sectional areas (CSAs) of the external nares and nasal vestibule on computed tomography (CT) transverse sections in brachycephalic dogs that underwent a vestibuloplasty in combination with either a Trader's technique or vertical wedge resection rhinoplasty.

Methods: Medical records and corresponding preoperative and postoperative head computed tomographic images were reviewed to measure CSA at two defined points on transverse sections of the nasal cavities.

Results: Nine brachycephalic dogs were included for analysis.

Early Prediction of Hypertensive Disorders of Pregnancy Using Machine Learning and Medical Records from the First and Second Trimesters.

Hypertensive disorders of pregnancy (HDPs) remain a major challenge in maternal health. Early prediction of HDPs is crucial for timely intervention. Most existing predictive machine learning (ML) models rely on costly methods like blood, urine, genetic tests, and ultrasound, often extracting features from data gathered throughout pregnancy, delaying intervention.