Hematology

GRACE: Unveiling Gene Regulatory Networks With Causal Mechanistic Graph Neural Networks in Single-Cell RNA-Sequencing Data.

Reconstructing gene regulatory networks (GRNs) using single-cell RNA sequencing (scRNA-seq) data holds great promise for unraveling cellular fate development and heterogeneity. While numerous machine-learning methods have been proposed to infer GRNs from scRNA-seq gene expression data, many of them operate solely in a statistical or black box manner, limiting their capacity for making causal infer...

Model-Based Convolution Neural Network for 3D Near-Infrared Spectral Tomography.

Near-infrared spectral tomography (NIRST) is a non-invasive imaging technique that provides functional information about biological tissues. Due to diffuse light propagation in tissue and limited boundary measurements, NIRST image reconstruction presents an ill-posed and ill-conditioned computational problem that is difficult to solve. To address this challenge, we developed a reconstruction algor...

Probiotic Effects of and on the Growth, Health, and Microbiota of Red Tilapia ( sp.).

This study evaluated the effects of a commercial probiotic containing and on the growth performance, intestinal histological structure, body composi...

Insulin resistance in type 1 diabetes is a key modulator of platelet hyperreactivity.

AIMS/HYPOTHESIS: Individuals with type 1 diabetes are at increased cardiovascular risk, particularly in the presence of insulin resistance. A prothrom...

Clinical Validation of a Noninvasive Multi-Omics Method for Multicancer Early Detection in Retrospective and Prospective Cohorts.

Recent studies highlight the promise of blood-based multicancer early detection (MCED) tests for identifying asymptomatic patients with cancer. Howeve...

Microbiome and fragmentation pattern of blood cell-free DNA and fecal metagenome enhance colorectal cancer micro-dysbiosis and diagnosis analysis: a proof-of-concept study.

Colorectal cancer (CRC) is the third most common cancer, and it can be prevented by performing early screening. As a hallmark of cancer, the human mic...

Evaluation of Six Large Language Models for Clinical Decision Support: Application in Transfusion Decision-making for RhD Blood-type Patients.

BACKGROUND: Large language models (LLMs) have the potential for clinical decision support; however, their use in specific tasks, such as determining t...

Detection of β-Thalassemia trait from a heterogeneous population with red cell indices and parameters.

BACKGROUND: India is home to about 42 million people with β-thalassemia trait (βTT) necessitating screening of βTT to stop spread of the disease. Over...

Phytophagous, blood-suckers or predators? Automated identification of Chagas disease vectors and similar bugs using convolutional neural network algorithms.

Correct identification of blood-sucking bugs, such as triatomines, is important because they are vectors of Chagas' disease. Identifying these insects...

Multiscale deformed attention networks for white blood cell detection.

White blood cell (WBC) detection is pivotal in medical diagnostics, crucial for diagnosing infections, inflammations, and certain cancers. Traditional...

Machine learning-based prediction of bleeding risk in extracorporeal membrane oxygenation patients using transfusion as a surrogate marker.

BACKGROUND: The increasing use of extracorporeal membrane oxygenation (ECMO) has highlighted challenges in managing bleeding complications. Optimal tr...

Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images.

Bone marrow (BM) plays a crucial role in the hematopoietic process, producing all of the body's blood cells and maintaining the overall immune and hea...

Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction.

Biological age (BA) and frailty represent two distinct health measures that offer valuable insights into the aging process. Comparing and analyzing bl...

Optimizing machine learning models for predicting anemia among under-five children in Ethiopia: insights from Ethiopian demographic and health survey data.

BACKGROUND: Healthcare practitioners require a robust predictive system to accurately diagnose diseases, especially in young children with conditions ...

Investigating handheld near-infrared spectroscopy for forensic body fluid analysis.

Forensic casework and crime scene examination will often involve the identification and analysis of biological evidence found on a wide variety of sur...

Investigating CAR-T Treatment Access for Multiple Myeloma Patients Using Real-World Evidence.

OBJECTIVE: Multiple myeloma (MM) is the second most common hematologic malignancy in the U.S., with Black patients being diagnosed at twice the rate o...

Integrated transcriptomics of multiple sclerosis peripheral blood mononuclear cells explored potential biomarkers for the disease.

BACKGROUND: Despite their importance, blood RNAs have not been comprehensively studied as potential diagnostic markers for multiple sclerosis (MS). He...

A comprehensive case study of deep learning on the detection of alpha thalassemia and beta thalassemia using public and private datasets.

This study explores the performance of deep learning models, specifically Convolutional Neural Networks (CNN) and XGBoost, in predicting alpha and bet...

Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.

BackgroundHospitalized patients with blood cancer face an elevated risk for cardiovascular diseases caused by cardiotoxic cancer therapies, which can ...

Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests.

Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability and death worldwide. Transient ischemic attack (TIA)...