Hematology

FDG-PET Medullary Total Tumor Volume Highlights High-Risk Newly Diagnosed Multiple Myeloma Patients in CASSIOPEIA Trial.

This study aimed to assess the prognostic value of medullary total metabolic tumor volume (mTMTV) derived from fluorodeoxyglucose-positron emission tomography/computed tomography ([18F]FDG-PET/CT) compared with conventional PET-derived features and biological/chromosomal abnormalities in patients with newly diagnosed multiple myeloma (NDMM) treated with daratumumab for induction/consolidation and/...

Beyond Percutaneous Coronary Intervention - Targeted Molecular Therapies for the Next Era of Coronary Care.

Coronary artery disease (CAD) remains the leading global cause of cardiovascular morbidity and mortality, driven by dysregulated lipid metabolism, chronic vascular inflammation, thrombosis, and genetic susceptibility. Management has long relied on mechanical revascularization-percutaneous coronary intervention and coronary artery bypass grafting supported by antithrombotic therapy. While these res...

A machine learning approach using echocardiographic and hematological parameters to classify cerebral small vessel disease burden.

BACKGROUND: Cerebral small vessel disease (CSVD) is a leading cause of stroke and dementia and is associated with cardiac and hematological biomarkers...

GHMS-CycleGAN: Graph-Based Hierarchical Multi-stain CycleGAN for Stain Normalization and Classification in Digital Pathology.

The emergence of digital pathology (DP) has provided numerous computer-aided diagnosis (CAD) opportunities and tools based primarily on deep learning ...

Screening for Alzheimer's disease in the community using an AI-driven screening platform: design of the PREDICTOM study.

BACKGROUND: Recent developments in physiological, imaging and digital biomarkers combined with the approval of new disease-modifying drugs against Alz...

Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.

Hypertension remains the most prevalent modifiable risk factor for cardiovascular morbidity and mortality worldwide, yet rates of effective blood pres...

Perioperative blood loss in cardiac surgery: Validation of machine learning-derived clusters.

BackgroundThere is no universally accepted definition of perioperative blood loss in cardiac surgery. Existing methods are based on chest tube output ...

Cardiovascular outcomes and subclinical atherosclerosis in metabolic-associated steatotic liver disease: long-term follow-up from a specialized outpatient cohort.

BACKGROUND AND AIMS: Cardiovascular disease is the main cause of mortality in metabolic-associated steatotic liver disease (MASLD). This study evaluat...

GRNFormer: Accurate Gene Regulatory Network Inference Using Graph Transformer.

MOTIVATION: Deciphering gene regulatory networks (GRNs) from single-cell transcriptomics data remains a fundamental challenge in computational biology...

MuCB-tabpfn: A multimodal feature fusion framework for predicting human blood concentrations of organic pollutants.

The accurate prediction of chemical concentrations in human blood is essential for evaluating health risks associated with synthetic organic pollutant...

Personalized prediction of initial valproic acid dose in children with epilepsy using machine learning techniques.

INTRODUCTION: Valproic acid (VPA) is widely prescribed antiepileptic drug in children because of its broad-spectrum efficacy. However, marked inter-in...

Integrating Mechanistic Modeling and Machine Learning to Study CD4+/CD8+ CAR-T Cell Dynamics with Tumor Antigen Regulation.

Chimeric antigen receptor (CAR) T cell therapy has shown remarkable success in hematological malignancies, yet patient responses remain highly variabl...

Evaluation of explainable machine learning models for predicting mid-term stone recurrence after percutaneous nephrolithotomy: a retrospective observational cohort study.

OBJECTIVE: To develop an explainable machine learning (ML) model to predict mid-term (3 years) stone recurrence (SR) after percutaneous nephrolithotom...

Development of hematopoietic stem cell-targeted lipid nanoparticles through lipid composition optimization.

Hematopoietic stem cell (HSC)-targeted gene editing holds significant potential for treating hereditary hematopoietic disorders, yet the efficient and...

EXPRESS: Identification of Acetylation-Related Molecular Signatures in Knee Osteoarthritis Patients with Significant Response to Warm-Needle Acupuncture Using Machine-Learning Approaches.

OBJECTIVE: This study aims to delineate remodeling effects of acetylation-related gene expression in knee osteoarthritis (KOA) patients markedly respo...

Machine learning-based predictive models and subtypes patterns in peripheral blood of schizophrenia based on a machine learning computational framework.

Schizophrenia (SCZ) is a complex psychiatric disorder, and its pathogenic mechanisms are not yet fully understood. The identification of reliable bloo...

Development and internal validation of a machine learning-based model to predict 1-year all-cause mortality in patients with gastrointestinal bleeding with type 2 diabetes.

Development and validation of an interpretable machine learning model integrating inflammatory blood biomarkers for predicting postoperative breast cancer-related lymphedema: A prospective cohort study.

BACKGROUND: Breast cancer-related lymphedema is a frequent and debilitating complication. Early identification of high-risk individuals is critical, y...

A MULTIMODAL STOOL RNA, FIT AND MACHINE LEARNING CONCEPT FOR DETECTION OF ADVANCED PRECANCEROUS LESIONS AND COLORECTAL CANCER.

Accurate and effective early detection of colorectal cancer (CRC) and advanced precancerous lesions (APLs) is still a challenge. The purpose of this s...

A deep learning pipeline for mapping in situ network-level neurovascular coupling in multi-photon fluorescence microscopy.

Functional hyperemia is a well-established hallmark of healthy brain function, whereby local brain blood flow adjusts in response to a change in the a...