Hematology

Fetal and neonatal alloimmune thrombocytopenia (FNAIT): From reactive transfusion to proactive prevention.

Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a major cause of severe thrombocytopenia, intracranial hemorrhage, and long-term neurological impairment in otherwise healthy neonates. Current management strategies are largely reactive, relying on maternal intravenous immunoglobulin (IVIG), intrauterine platelet transfusions, and postnatal supportive care. Although these interventions can...

Predicting infected pancreatic necrosis in acute pancreatitis using machine learning models and feature selection.

Infected pancreatic necrosis (IPN) is a life-threatening complication of acute pancreatitis (AP), and its early prediction remains challenging. This study aimed to develop and externally validate interpretable machine learning models for individualized IPN risk prediction. A total of 728 patients with AP admitted to Xuanwu Hospital, Capital Medical University, between 2017 and 2023 were retrospect...

A Multi-Omics and machine learning platelet-related prognostic signature in multiple myeloma.

Machine learning estimation of FVIII pharmacokinetic parameters in Chinese children with severe Hemophilia A.

Hemophilia A is a rare inherited bleeding disorder typically managed with coagulation factor VIII (FVIII) replacement therapy. Designing personalized ...

Outcomes with revascularization vs. medical therapy according to plaque burden from coronary computed tomography angiography.

AIMS: We aimed to investigate whether plaque burden from coronary computed tomography angiography (CCTA) could be used to identify patients potentiall...

Deep Learning Model to Detect Diabetic Retinopathy in 45° Images Using Ground Truth from Ultra-Widefield Imaging.

PURPOSE: To evaluate the performance of a deep learning (DL) model in classifying diabetic retinopathy (DR) severity using fundus images with varying ...

Predicting Nocturnal Hypertension in CKD Through Table-Value Diffusion Model.

BACKGROUND: Ambulatory blood pressure monitoring is indispensable for diagnosing nocturnal hypertension (NH) among patients with chronic kidney diseas...

msCNN-PLM-FAD: Enhanced predicting FAD binding sites by using protein language representation combined with multiple separable windows convolutional neural networks.

The FAD binding sites classification problem is crucial because it is directly related to many diseases, such as flavoprotein-associated diseases, dev...

Deep learning assisted cell electrical signal analysis in impedance cytometry.

In this study, we developed BioFluxNet, a 1D CNN-based algorithm for automated analysis of raw electrical signals in impedance cytometry to directly c...

Ensemble model assisted classification of gastrointestinal bleeding using wireless capsule endoscopy.

Wireless Capsule Endoscopy (WCE) is a useful method for imaging the intestines painlessly and looking into gastrointestinal tract diseases. The invest...

Exploring stress and coping among black women in early midlife with elevated blood pressure in a safety-net health system: a qualitative focus group protocol.

INTRODUCTION: Black women in early midlife experience disproportionate exposure to stress and elevated cardiovascular risk, including hypertension. De...

Construction of interpretable machine-learning diagnostic models for erectile dysfunction based on routine blood and biochemical detection data.

Despite its high incidence, the diagnosis of erectile dysfunction (ED) is impeded by current diagnostic constraints and patient hesitancy in seeking m...

Interpretable machine learning for chronic kidney disease prediction: Insights from SHAP and LIME analyses.

Chronic kidney disease (CKD) is a progressive condition requiring early detection for optimal patient outcomes. This study developed an interpretable ...

Targeting signaling pathways in lymphoma: From molecular mechanisms to clinical breakthroughs.

The treatment paradigm for lymphoma, a highly heterogeneous group of hematologic malignancies, has been revolutionized by the development of therapies...

Pure Chitosan Microfluidic Spinning Affords Modular Core-Sheath Fibers and Hand-Crafted Scaffolds with Enhanced Fibroblast Compatibility.

Microfluidic-spun hydrogel fibers are appealing for tissue engineering and cell transplantation applications, as they give access to fiber-shaped tiss...

Machine learning integration of serial blood biomarkers enhances cognitive decline prediction in early Parkinson's disease.

Cognitive decline is a major non-motor complication in early Parkinson's disease (PD), but predicting its progression remains challenging. Using data ...

Early detection of chronic kidney disease based on a SURD-enhanced machine learning model.

Chronic kidney disease (CKD) represents a major global health burden, and early, reliable risk prediction remains clinically challenging. This study p...

Improving acute lymphoblastic leukemia diagnosis through CBAM-enhanced VGG19 deep learning.

Acute Lymphoblastic Leukemia (ALL) is an aggressive blood cancer that requires rapid and accurate diagnosis. Manual review of blood and bone marrow sm...

A machine learning model integrating circulating Temra cell transcriptional profiles to predict immunotherapy efficacy.

BACKGROUND: While immunotherapy has transformed treatment paradigms for cancer, durable response rates remain below 30%. Currently available biomarker...

Mitigating mammalian cell cytotoxicity of drinking water upon chlorination by conventional and advanced treatment processes.

Ozone (O3)-biological activated carbon (BAC) advanced treatment is increasingly applied after conventional coagulation-sedimentation for controlling t...