Hematology

Machine Learning Approach Enables Highly Accurate Identification of At-Risk Metabolic Dysfunction-Associated Steatohepatitis.

AIM: At-risk metabolic dysfunction-associated steatohepatitis (MASH), characterized by significant activity and fibrosis, increases the risk of liver complications. Liver stiffness measurement (LSM), commonly used to detect significant fibrosis, has limitations in terms of accessibility and performance in certain populations. We aimed to develop a simple-to-use machine learning (ML) model to ident...

Assessing Health Care Professionals' Perceptions of a New System in Clinical Workflows: Systems Engineering Initiative for Patient Safety-Based Consensual Qualitative Research.

BACKGROUND: Artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) are increasingly embedded within electronic health record (EHR) environments; however, their introduction can disrupt existing workflows and raise patient safety concerns, particularly in high-stakes settings such as surgical transfusion. Limited qualitative evidence exists regarding how frontline profession...

An annotated dataset of Gram stains from positive blood cultures.

Bloodstream infections (BSIs) of high morbidity and mortality are across all age groups, and urgent for accurate intervention. Gram stain interpretati...

Evaluation of the neurotrophic peptide mixture in pathogenetic therapy of patients with Parkinson's disease.

This exploratory, single-group, open-label study investigated 17 patients with Parkinson's disease (PD) using a pre-post design. Motor and non-motor o...

Prediction of renal cell carcinoma: Development and validation of machine learning model.

Renal cell carcinoma (RCC) is the leading cause of urinary system morbidity and mortality. Early identification is crucial for improving RCC patient o...

Machine learning-based LASSO-Cox model for dementia prediction: the role of midlife cardiometabolic, inflammatory, and genetic risk factors in a US cohort.

PURPOSE: We aimed to identify key midlife dementia predictors and develop a novel machine learning (ML) -enabled risk prediction model. METHODS: Using...

Interpretable machine learning-driven QSAR modeling for coagulation factor X inhibitors: from molecular descriptors to predictive potency.

Inhibition of Coagulation Factor X (FXa) is a clinically validated therapeutic strategy; however, developing safer and more selective inhibitors remai...

Explainable AI framework for improved Thalassemia mental health classification and feature selection.

Mental health challenges in Thalassemia patients are often overlooked, despite their significant impact on quality of life. Traditional statistical an...

Incidence and implications of abstinence-induced recompensation in alcohol-related cirrhosis.

BACKGROUND & AIMS: Alcohol abstinence enables hepatic recompensation in patients with decompensated alcohol-related cirrhosis. This study investigated...

Lung scintigraphy for the diagnosis of acute pulmonary embolism.

Pulmonary embolism (PE) remains a major diagnostic challenge due to its potentially life-threatening nature and the clinical burden associated with an...

Proteomics Profiling Reveals Circulating Biomarkers and Dysregulated Pathways in Transthyretin Amyloid Cardiomyopathy.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) causes a restrictive cardiomyopathy resulting in heart failure (HF). Signaling pathways ass...

Integrated network toxicology, machine learning, and bioinformatics analysis reveals sodium dehydroacetate-induced coagulation dysfunction in colorectal cancer.

Coagulation dysfunction, a common hematologic disorder with unclear pathogenesis, is influenced by environmental factors. Sodium dehydroacetate (SDA),...

Prehospital transfusion strategies in haemorrhagic shock.

BACKGROUND: Massive transfusion protocols are established in-hospital practices for managing haemorrhagic shock, yet critical bleeding accounts for up...

Assessing in-hospital mortality risk in ICU lung cancer patients using machine learning: An analysis based on the MIMIC-IV database.

BACKGROUND: Patients with advanced lung cancer admitted to the intensive care unit (ICU) face a substantially elevated risk of in-hospital mortality. ...

Artificial intelligence and machine learning driven nanorobotics for targeted brain delivery: Redefining blood-brain barrier navigation.

The blood-brain barrier (BBB) plays a central role in preserving central nervous system (CNS) homeostasis, and its dysfunction is implicated in variou...

Clinical evaluation and bone loss prediction of titanium-zirconium implants: A retrospective study of 1-5-year follow-up.

OBJECTIVES: To evaluate the clinical and radiographic outcomes of titanium-zirconium implants, identify risk factors and develop predictive models for...

Using casual inference and machine learning with exposure determinant modeling to identify important workplace controls.

OBJECTIVES: Exposure determinant modeling can help industrial hygienists understand where, when, and how to control occupational exposures for their p...

A multimarker panel for diagnosis and prognosis prediction of fungal infections.

To develop and validate a routine indicator-based multimarker panel for fungal infection (FI) diagnosis and prognosis prediction. A retrospective coho...

STF: A Unified Framework for Joint Pixel-Level Segmentation and Tracking of Tissues in Endoscopic Surgery.

Endoscopic minimally invasive surgery relies on precise tissue video segmentation to avoid complications such as vascular bleeding or nerve injury. Ho...

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.

BACKGROUND: Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly ...