Hematology

A study on risk factors for the progression from T2DM to end-stage renal disease based on Mendelian randomization and logistic regression analysis.

Using two-sample Mendelian randomization (MR) based on GWAS data from the IEU OpenGWAS project and a retrospective clinical cohort, this study investigated risk factors for progression from type 2 diabetes mellitus (T2DM) to end-stage renal disease (ESRD) and developed a predictive model. The T2DM GWAS dataset (ebi-a-GCST010118; 2020) included 433,540 individuals (77,418 cases and 356,122 controls...

An explainable machine learning model for predicting in-hospital infection in patients with systemic lupus erythematosus.

Infection is a leading cause of mortality in patients with systemic lupus erythematosus (SLE), yet effective tools for early identification of high-risk patients are lacking. This study aimed to develop an explainable machine learning (ML) model to predict in-hospital infection risk among SLE patients. We analyzed adult patients (≥18 years) with SLE (n = 7,833) from three departments using a popul...

AI-Enabled Automated Schistocyte Classification in Peripheral Blood for TMA Auxiliary Diagnosis.

BACKGROUND: Schistocytes are critical morphological markers for thrombotic microangiopathy (TMA) diagnosis. Manual identification is hampered by incon...

Beyond the guidelines: Vascular function as the central determinant and therapeutic target in hypertension.

Hypertension has traditionally been defined and managed according to brachial blood pressure levels. Although this pressure-centric strategy has signi...

From prediction to practice: Barriers to implementing artificial intelligence in blood inventory management and transfusion support.

BACKGROUND: Artificial intelligence (AI) is increasingly applied to blood-demand forecasting, donor management, inventory optimisation, wastage reduct...

The FERM guild: a differentially correlated microbial module drives hypertension via metabolic flux perturbations.

UNLABELLED: Hypertension is a major risk factor for cardiovascular diseases, with changes in gut microbiota composition and function being closely ass...

Prediction of Blood Transfusion Need and Dose in Patients With Upper Gastrointestinal Bleeding: Retrospective Multicenter Prediction Model Study.

BACKGROUND: Transfusion thresholds in upper gastrointestinal bleeding are debated; hemoglobin cutoffs of 70-80 g/L are widely cited yet inconsistently...

Bo-Net: Deep learning-based model for automatic bone stromal cell segmentation of fluorescence microscopy images.

BACKGROUND AND OBJECTIVES: Bone is a multicellular organ that is the site of complex pathophysiological events (such as cancer). 3D confocal and multi...

A machine learning-based framework for predicting type 2 diabetes mellitus using hematological indices.

OBJECTIVE: This study aimed to develop a machine learning (ML) framework to predict incident type 2 diabetes mellitus (T2DM) using routinely available...

Performance, Safety, and Patient Experience of an Autonomous Robotic Phlebotomy Device: A Multicenter Trial.

BACKGROUND: Automated phlebotomy has the potential to improve patient outcomes and address phlebotomist workforce challenges. The Autonomous Blood Dra...

Practical Optimization of Deep Learning Models for Cuffless Blood Pressure Estimation From Photoplethysmography.

To improve deep learning-based cuffless blood pressure (BP) estimation from photoplethysmography (PPG) for continuous, non-invasive monitoring wh...

Peripheral blood profiles reflecting progenitor lineage balance are associated with molecular response in chronic myeloid leukemia.

Early achievement of deep remission improves patients' outcome in chronic myeloid leukemia (CML) treatment, highlighting the need for predictive indic...

MRI-based oxygen extraction fraction mapping using qBOLD, QSM, and hybrid QSM-qBOLD methods: Methodological advances, validation, and clinical applications.

Oxygen extraction fraction (OEF) is a physiological parameter reflecting the fraction of delivered arterial oxygen extracted by cerebral tissue, provi...

Prediction of coronary atherosclerosis progression in type 2 diabetes mellitus based on AI-derived CCTA parameters and clinical factors: a follow-up study.

OBJECTIVE: To develop a predictive model for coronary atherosclerosis progression in patients with type 2 diabetes mellitus (T2DM) based on Artificial...

Machine learning-based prediction of Clavien-Dindo ≥ II complications after ureteroscopy.

PURPOSE: Clavien-Dindo (CD) grade II complications after ureteroscopy occur in approximately 13-17% of cases, typically involving infections or bleedi...

Enhancing machine learning-based binder design with high-throughput screening: A comparison of mRNA and yeast display technologies.

Recent advances in machine learning (ML)-based protein design methods have enabled the rapid in silico generation of large libraries of miniprotein bi...

Machine Learning Predicts Treatment Response and Prognostic Pathways From Whole-Blood Transcriptome in Primary Biliary Cholangitis.

BACKGROUND AND AIMS: Prognostic biomarkers that link disease progression and/or responses to therapeutic interventions in patients with primary biliar...

Prediction Model for Mild Cognitive Impairment in Older Chinese Patients With Cerebral Small Vessel Disease Based on XGBoost Algorithms and Shapley Additive Explanations.

OBJECTIVE: This study aims to evaluate cognitive function in patients with Cerebral Small Vessel Disease (CSVD) and investigate its association with v...

Development and Validation of an Interpretable Machine Learning-Based Clinical Prediction Model for Short-Term Mortality in Intracerebral Hemorrhage With Thrombocytopenia: A Multicenter Study.

BACKGROUND: Intracerebral hemorrhage (ICH) with thrombocytopenia is associated with poor outcomes, but early risk prediction tools for this subgroup a...

A tabular prior-data fitted network-based prediction model integrating ferritin and total iron-binding capacity for 30-day all-cause mortality in critically ill cancer patients.

OBJECTIVES: Accurately predicting outcomes for critically ill cancer patients remains challenging. This study aimed to integrate biologically relevant...