Validation of uPath HER2 dual-colour dual in-situ hybridisation image analysis tool for HER2/neu testing in breast cancer.

Aims: gene is amplified in 15%-20% of invasive breast cancers (IBCs), serving as critical prognostic and predictive marker. -targeted therapies have improved outcomes for -positive patients, highlighting the importance of accurate assessment. Immunohistochemistry is commonly used for screening overexpression, with equivocal cases reflex tested using in situ hybridisation (ISH) methods like fluorescence (FISH) or dual-colour dual ISH (D-DISH).

Safety and immunogenicity of the invasive non-typhoidal Salmonella (iNTS)-GMMA vaccine: a first-in-human, randomised, dose escalation trial.

Background: Invasive non-typhoid Salmonella (iNTS) is a leading cause of morbidity and mortality in sub-Saharan Africa. We assess the safety and immunogenicity of an outer membrane vesicle vaccine (iNTS-GMMA) derived from the two most common serovars, S. Enteritidis (SEn) and S.

Detection and quantification of fluorescein angiography leakage: From manual grading to advances in machine learning.

Fluorescein angiography (FA) has long been a cornerstone for evaluating retinal vascular leakage in diseases like uveitis, diabetic retinopathy, and macular degeneration, but its interpretation relies on subjective grading that can vary between clinicians. With the emergence of artificial intelligence (AI), there is a push to transform this qualitative assessment into objective, quantifiable metrics. We conducted a comprehensive literature search using PubMed, Embase, and Scopus, combining keywords and MeSH terms related to fluorescein angiography leakage, artificial intelligence, and retinal vascular diseases.

Role of artificial intelligence-based ocular biomarkers in hepatobiliary diseases: A scoping review.

Artificial intelligence (AI) has become an indispensable tool in modern health care, offering transformative potential across clinical workflows and diagnostic innovations. This review explores the sation of AI technologies in synthesizing and analyzing multimodal data to enhance efficiency and accuracy in health care delivery. Specifically, deep learning models have demonstrated remarkable capabilities in identifying seven categories of hepatobiliary disorders using ocular imaging datasets, including slit-lamp, retinal fundus, and optical coherence tomography images.

Association between body mass index and the risk of bacterial vaginosis: a systematic review and meta-analysis.

Introduction: Bacterial vaginosis, resulting from vaginal microbiota imbalance and lactobacilli depletion, is the leading cause of abnormally appearing discharge in reproductive-aged women. Bacterial vaginosis is also associated with risk for sexually transmitted infections, preterm birth, and pelvic inflammatory disease. This meta-analysis assessed the association between the risk of bacterial vaginosis (BV) and body mass index (BMI).