FDA General

Learner-Centered Microbiology Teaching via the Flipped Classroom Model: Insights from a Mixed-Methods Educational Study.

CONTEXT: Medical education has evolved to emphasize active learning and technology for competency development. The flipped classroom (FCR) model shifts from teacher-centered to learner-centered instruction, promoting deep learning through self-study and interactive classroom engagement. This study explores the implementation of FCR in Phase II medical microbiology education, examining its enabling...

Structure-Based Prediction of QT Prolongation Risk Using Graph Neural Networks: An Integrative Approach Combining In Vitro hERG Assays and Pharmacovigilance Data.

Drug-induced QT-interval prolongation, a non-specific biomarker of increased risk for Torsades de Pointes (TdP), is a major safety concern in drug development. While in vitro hERG inhibition assays are required for early-phase screening, pharmacovigilance data from sources like the FDA Adverse Event Reporting System (FAERS) provide complementary insights. Integrating these heterogeneous data with ...

From black box to clear box: explainable AI for next-gen pharmacovigilance.

INTRODUCTION: Pharmacovigilance (PV) plays a vital role in post-marketing surveillance of drug safety; however, traditional methods are hindered by un...

Artificial intelligence-powered analysis of operating room turnover: impact of instrument burden.

OBJECTIVE: Improved operating room (OR) efficiency provides greater patient throughput, reduced costs, and maximal patient care. The aim of this study...

Deep phenotyping of suicidal ideation after discharge from psychiatric inpatient care: study protocol for an interdisciplinary, multicentre prospective observational study in Psychiatric University Hospitals.

INTRODUCTION: Suicidal thoughts and behaviours (STB) are a critical public health concern, with 700 000 deaths by suicide each year. The period immedi...

Auto-LSN: fully automated liver surface nodularity quantification in CT based on deep learning for the evaluation of advanced chronic liver disease.

OBJECTIVES: Liver surface nodularity (LSN) is a recognized non-invasive biomarker of cirrhosis. This study introduces auto-LSN, an artificial intellig...

Exploring Clinician Perspectives on Artificial Intelligence in Primary Care: Qualitative Systematic Review and Meta-Synthesis.

BACKGROUND: Recent advances have highlighted the potential of artificial intelligence (AI) systems to assist clinicians with administrative and clinic...

Plasmonic artificial inspector for herbal medicines via surface-enhanced Raman spectroscopy and deep learning.

For the safety monitoring of herbal medicines (HMs), HM inspectors carry out an organoleptic examination before acceptance for market supply. The orga...

Optimizing vedolizumab therapy in ulcerative colitis: A critical synthesis of trial evidence and the emerging role of artificial intelligence.

BACKGROUND: Vedolizumab, a monoclonal antibody targeting the α4β7 integrin, offers gut-selective immunosuppression and represents a cornerstone biolog...

Pioneering the future: Delivering the first digitally generated CMC post-approval change dossier to global regulators simultaneously.

As medicinal products and their manufacturing processes evolve, biopharmaceutical companies must continuously manage, document, and submit post-approv...

Interpretable machine learning-driven identification of novel DENV NS2B-NS3 protease inhibitors through multi-stage virtual screening and experimental validation.

Dengue fever is a mosquito-borne viral infection caused by dengue virus (DENV). It has emerged as a worldwide health problem, afflicting millions of p...

Protocol for Personalised Prediction of Persistent Postsurgical Pain.

INTRODUCTION: Persistent postsurgical pain (PPSP) affects up to 15% of patients after major surgery, impairing physical function, quality of life and ...

HappyMums mobile application study protocol: use of a smartphone application to gather data predictive of antenatal depression.

INTRODUCTION: Mobile health (mHealth) technologies have become increasingly popular for monitoring mental health symptoms and lifestyle behaviours, an...

Efficient target detection method based on wavelet transform and progressive feature pyramid network: a case study of power grid inspection.

In contemporary society, electricity is a critical resource integral to national economies and public welfare, making the safe and stable operation of...

Transcriptomic analyses reveal regulatory plasticity and metabolic reprogramming underlying genotype-specific microspore embryogenesis in wheat.

Embryogenic efficiency in wheat microspores is driven by epigenetic regulation, homoeolog expression bias, and genotype-specific genomic variation, wi...

Artificial Intelligence-Assisted Detection of Amyloid-Related Imaging Abnormalities: Promise and Pitfalls.

The advent of anti-amyloid therapies (AATs) for Alzheimer disease (AD) has elevated the importance of MRI surveillance for amyloid-related imaging abn...

Implementation of AI systems in the clinical laboratory: insights from an expert survey and recommendations for best practice.

OBJECTIVES: Despite growing interest in artificial intelligence (AI) and machine learning (ML), many laboratory professionals lack experience with dev...

Development and multicentre validation of an artificial intelligence electrocardiogram model for ventricular remodeling in repaired tetralogy of Fallot.

AIMS: Periodic cardiac MRI (CMR) is recommended to identify adverse ventricular remodelling in repaired tetralogy of Fallot (TOF), but access to CMR i...

Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and electronic population-based healthcare data.

INTRODUCTION: Within the UK there are 33 deaths every day from prostate cancer, second only to lung cancer as the most common cause of cancer death in...

Integrating simplified Swin-T with modified EFS-Net for attention-guided underwater pipelines segmentation in complex underwater environments.

Accurate segmentation of underwater pipelines is essential for marine infrastructure inspection. However, deep learning models often struggle with ext...