Emergency Medicine

Integrated network toxicology and computational profiling of acetyl tributyl citrate-related mechanisms in Alzheimer's disease.

BackgroundAcetyl tributyl citrate (ATBC), an eco-friendly plasticizer, exhibits poorly characterized neurotoxic effects.ObjectiveWe integrated network toxicology, machine learning, and molecular docking to elucidate molecular mechanisms underlying the link between ATBC exposure and Alzheimer's disease (AD) pathogenesis.MethodsPotential action targets of ATBC were screened from ChEMBL, TargetNet, a...

Forecasting emergency department visits in the reference hospital of the Balearic Islands: The role of tourist and weather data.

Accurate forecasting of patient arrivals at emergency departments (EDs) is vital for efficient resource allocation and high-quality patient care. In this study we investigate the relevance of exogenous variables, namely tourism, weather, calendar and demographic variables, in forecasting ED visits in the reference hospital in Palma de Mallorca, a city with significant seasonal population fluctuati...

A complete blood count-based machine learning model for rapid differentiation of aplastic anemia, immune thrombocytopenia, and myelodysplastic syndromes in routine clinical practice.

BACKGROUND: Accurate differentiation of common hematologic disorders remains challenging in routine clinical practice and often requires invasive diag...

Development and temporal validation of an interpretable point score for in-hospital mortality in CLL/SLL using the U.S. National Inpatient Sample, 2016-2022.

In-hospital mortality among patients with chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) is ∼6%, yet no validated, transparent beds...

Evidence-based AI clinical decision support system for acute burn care and complex reconstruction.

BACKGROUND: Burn surgery requires rapid, evidence-based decision-making across acute resuscitation, operative management, and reconstruction. Despite ...

Cardiogenic Shock Revisited: From Hemodynamic-Centric to Multimodal Precision Care.

Cardiogenic shock (CS) remains a major clinical challenge, with persistently high short-term mortality despite advances in reperfusion therapy, pharma...

LLM-based keyword augmentation for title-driven evidence selection: A practical approach.

Keyword-based search is widely used in digital forensic investigations, yet its effectiveness depends strongly on investigator experience, leading to ...

Can algorithms come to the rescue of a failing heart? Machine learning, artificial intelligence, and decision-making in cardiogenic shock.

Detecting multimorbidity patterns in Alzheimer's disease using unsupervised machine learning: A nationwide emergency department study (2007-2022).

BackgroundAlzheimer's disease (AD) patients frequently present to emergency departments (EDs) with complex comorbidities that complicate triage and ma...

Unveiling m7G modification patterns and causal drivers governing intracranial aneurysm rupture risk through multi-omics validation and m7G-MeRIP-seq profiling.

Intracranial aneurysm (IA) rupture causes severe brain hemorrhage with high mortality, yet its molecular drivers remain unclear and better risk predic...

Artificial intelligence for diagnosis and triage in oral cancer: a clinician‑centered narrative review.

BACKGROUND: Early diagnosis of oral squamous cell carcinoma (OSCC) remains challenging, with survival largely stage-dependent at presentation. Artific...

DDFU-Net: A Deep Decoder-Focused U-Net Model for Retinal Lesion Segmentation.

Early detection of retinal lesions helps to avoid visual loss or blindness. The main lesions associated with eye diseases include soft exudates, hard ...

Impurities in Oncology Pharmaceuticals: A Review of Classification, Detection Methods, Regulatory Frameworks and Emerging Trends.

Pharmaceutical impurities pose a significant challenge in the development and manufacturing of anti-cancer drugs due to their high potency, narrow the...

Multi-omics investigation of benzo[a]pyrene in gastric cancer: comprehensive network toxicology, machine learning and molecular docking approaches.

Gastric cancer (GC) risk is shaped by environmental exposures such as benzo[a]pyrene (BaP). Here, we systematically identified BaP-toxicological targe...

Strategic Governance of Artificial Intelligence-Enabled Clinical Algorithm Development: Formative Evaluation of the Semiautomatic Clinical Algorithm Development Framework.

BACKGROUND: Health care leaders face a strategic dilemma: traditional expert-led content development ensures safety but is too slow for digital innova...

CT-based hybrid deep learning-radiomics framework for predicting postoperative rebleeding in hypertensive intracerebral hemorrhage.

OBJECTIVES: Hypertensive intracerebral hemorrhage (HICH) is a frequently encountered and highly lethal cerebrovascular disorder, and postoperative reb...

Deep-Fed: A comprehensive solution for precise bone fracture identification in athletes.

Bone fracture diagnosis is a critical aspect of sports medicine, where accurate and timely detection enables effective treatment and rapid recovery. T...

Exploring the mechanism of polymorphonuclear neutrophils against sepsis based on immune model.

BACKGROUND: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection and remains a major global health chall...

Preliminary assessment of AI as a triage tool for forensic toxicology case interpretation.

Large language models (LLMs) such as ChatGPT have demonstrated potential for interpretation in various scientific disciplines; however, their applicat...

Considerations for enhancing the clinical translational potential of LLM-Based TBI mortality prediction models.

This study explores the use of GPT-5 and traditional machine learning by scholars such as Tu et al. to predict the risk of emergency death in traumati...