Hematology

Prediction of Sepsis after Endourologic Kidney Stone Surgery: A Machine Learning Approach.

Sepsis secondary to urinary tract infection after kidney stone surgery is associated with considerable morbidity. Limited research examines the use of hemoglobin A1c (HbA1c) to predict postoperative sepsis after endourologic procedures. We developed a machine learning (ML) model trained on demographic and clinical data to predict postoperative sepsis and better identify patients requiring preoper...

Developing a Two-Dimensional Size-Exclusion Liquid Chromatography Platform for Isolating the High-Quality Extracellular Vesicles to Predict and Evaluate Metabolic Improvement Outcomes of Sleeve Gastrectomy.

The direct analysis of plasma exosomal components still presents significant challenges due to the interference of high concentrations of albumin, immunoglobulins, and other abundant proteins. Herein, we developed a two-dimensional size-exclusion liquid chromatography (2D-SELC) platform to isolate high-purity exosomes from human plasma for downstream lipidomics analysis. Compared to the traditiona...

Analysis of maternal fetal outcomes and complete blood count parameters according to the stages of placental abruption: a retrospective study.

BACKGROUND: To compare the demographic characteristics, maternal and perinatal outcomes and hemoglobin parameters according to stages diagnosed with p...

The utility of an artificial intelligence model based on decision tree and evolution algorithm to evaluate steatotic liver disease in a primary care setting.

Many ways of classifying steatotic liver disease (SLD) with metabolic conditions have been proposed. Thus, SLD-related variables were verified using a...

Advanced molecular approaches to thalassemia disorder and the selection of molecular-level diagnostic testing in resource-limited settings.

Beta-thalassemia is a genetic disorder that significantly burdens healthcare systems globally. This inherited blood disorder, categorized into beta-th...

The 2024 ESC guidelines on atrial fibrillation: essential updates for everyday clinical practice.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, and it is associated with substantial morbidity, mortality, and economic burden. Ef...

Machine learning application for bleeding risk prediction in patients with atrial fibrillation treated with oral anticoagulation.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with a significantly increased risk of systemic thromboembolism and stroke. Anti...

Association between metal mixture in urine and abnormal blood pressure and mediated effect of oxidative stress based on BKMR and Machine learning method.

BACKGROUND: Exposure to heavy metals represents a significant risk factor for hypertension and blood pressure disorders. Notably, current evidence ind...

Advanced design and classification of wearable near-infrared spectroscopy device using temporal channel reconfiguration multi-graph convolutional neural networks for motor activity.

In this paper, advanced design and classification of wearable near-infrared spectroscopy device using temporal channel reconfiguration multi-graph con...

28-day all-cause mortality in patients with alcoholic cirrhosis: a machine learning prediction model based on the MIMIC-IV.

To develop and validate a machine learning prediction model for 28-day all-cause mortality in patients with alcoholic cirrhosis using data from the MI...

DeepHeme, a high-performance, generalizable deep ensemble for bone marrow morphometry and hematologic diagnosis.

Cytomorphological analysis of the bone marrow aspirate (BMA) is pivotal for the diagnostic workup of a broad range of hematological disorders. However...

Direct-to-Consumer Testing: Relevance for Diabetes Therapy.

The performance of laboratory measurements by the people with diabetes (PwD) themselves ("direct-to-consumer testing"; DTCT) is conceptually not new f...

AI-Driven Blood Loss Prediction in Large-Volume Liposuction: Enhancing Precision and Patient Safety.

BACKGROUND: Over 2.3 million liposuctions are performed annually with a complication rate of about 5%, including a death rate of 1 in 5,000 due to blo...

Assessing serum thrombopoietin for enhanced diagnosis of ITP, AA, and MDS using machine learning: A retrospective cohort study.

Differentiating between immune thrombocytopenia (ITP), aplastic anemia (AA), and myelodysplastic syndromes (MDS) is critical due to the distinct treat...

Exploring the performance of an artificial intelligence- and morphology-driven workflow integrating 4 platelet enumeration technologies.

OBJECTIVE: Platelet count, one of the main parameters of the complete blood count, requires accurate evaluation to guide patient management. It can be...

Development and validation of an interpretable risk prediction model for the early classification of thalassemia.

Thalassemia is an inherited blood disorder. Current diagnostic methods mainly rely on sophisticated equipment and specifically trained technicians. Th...

Identification and validation of blood leukocyte DNA methylation biomarkers for early detection of colorectal neoplasm.

BACKGROUND: Identifying high-risk populations for colorectal cancer (CRC) is critical for precise screening. This study aimed to develop a novel risk ...

Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques.

The rapid decline of kidney function in middle-aged and elderly people has become an increasingly serious public health problem. Machine learning (ML)...

Optimization of hemocompatibility metrics in ventricular assist device design using machine learning and CFD-based response surface analysis.

Ventricular assist devices (VADs) are essential for end-stage heart failure patients, but their design must balance hydraulic efficiency and hemocompa...

Predicting carbapenem-resistant Pseudomonas aeruginosa infection risk using XGBoost model and explainability.

The prevalence and spread of carbapenem-resistant Pseudomonas aeruginosa (CRPA) is a global public health problem. This study aims to identify the ris...