Falls among geriatric cancer patients: a systematic review and meta-analysis of prevalence and risk across cancer types.

Background: Falls represent a significant health concern among the older adults, particularly geriatric cancer patients, due to their increased susceptibility from both age-related and cancer treatment-related factors. This systematic review and meta-analysis aimed to synthesize global data on the prevalence and risk of falls in this population to inform targeted fall prevention strategies.

Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive search of PubMed, Embase, and Web of Science up to October 2024.

Maternal hypertensive disorders among Individuals of reproductive age (15-49 Years) in South Asia: A global Burden of Disease study (1990-2021) with future Projections.

Background: Maternal hypertensive disorders (HDP) remain a major contributor to maternal and perinatal morbidity and mortality worldwide, particularly in South Asia, where healthcare disparities persist.

Objective: This study aims to analyze trends in maternal hypertensive disorders across South Asia from 1990 to 2021, leveraging data from the Global Burden of Disease (GBD) study to evaluate the effectiveness of healthcare interventions and provide actionable recommendations.

Methods: We conducted a retrospective analysis using GBD 2021 data for Bangladesh, Bhutan, India, Nepal, and Pakistan.

Staphylococcus borealis causing subclinical mastitis in dromedary camels.

Recent studies revealed increasing incidence of udder infections caused by Coagulase Negative Staphylococci (CNS) in dairy cattle, though studies in dromedary camel are meagre. The present report describes sub-clinical mastitis in two dromedary camels caused by Staphylococcus borealis, a recently recognized udder pathogen in cattle. The bacterium was identified on the basis of morphological and biochemical characteristics, MALDI-TOF MS analysis, 16s rRNA sequencing and BLAST.

Employing Automated Machine Learning (AutoML) Methods to Facilitate the ADMET Properties Prediction.

The rationale for using ADMET prediction tools in the early drug discovery paradigm is to guide the design of new compounds with favorable ADMET properties and ultimately minimize the attrition rates of drug failures. Artificial intelligence (AI) in ADMET modeling has gained momentum due to its high-throughput and low-cost attributes. In this study, we developed a machine learning model capable of predicting 11 ADMET properties of chemical compounds.