Hematology

Multiclass leukemia cell classification using hybrid deep learning and machine learning with CNN-based feature extraction.

Leukemia is the most prevalent form of blood cancer, affecting individuals across all age groups. Early and accurate diagnosis is crucial for effective treatment and improved clinical outcomes. Peripheral blood smear analysis, a key non-invasive diagnostic tool, often suffers from subjective interpretation, inter-observer variability, and a lack of readily available expertise. Although deep learni...

Unveiling etiology and mortality risks in community-acquired pneumonia: A machine learning approach.

Community-acquired pneumonia (CAP) is associated with high mortality, and accurate diagnosis and risk prediction are essential for improving patient outcomes. Traditional diagnostic methods have limitations, prompting the use of machine learning (ML) to enhance diagnostic precision and treatment strategies. This study aims to develop ML models to predict CAP etiology and mortality using clinical d...

Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection.

In general, deficient birth weight neonates suffer from hypoglycemia, and this can be quite disadvantageous. Like oxygen, glucose is a building block ...

Integrating bioinformatics and machine learning to investigate the mechanisms by which three major respiratory infectious diseases exacerbate heart failure.

Heart failure (HF) is a severe cardiovascular disease often worsened by respiratory infections like influenza, COVID-19, and community-acquired pneumo...

Machine learning model and hemoglobin to red cell distribution width ratio evaluates all-cause mortality in pulmonary embolism.

The ratio of hemoglobin (Hb) to red blood cell distribution width (RDW), known as HRR, functions as an innovative indicator related to prognosis. Howe...

Cervical cancer prediction using machine learning models based on routine blood analysis.

Cervical cancer (CC) is the fourth most common cancer among women globally. The key to preventing and treating CC is early detection, diagnosis, and t...

Detection of fasting blood sugar using a microwave sensor and convolutional neural network.

Monitoring of fasting blood sugar (FBS) is a critical component in the diagnosis and management of diabetes, one of the most widespread chronic diseas...

Machine Learning for Discriminating Microcytic Hypochromic Anemia Based on Erythrocyte Parameters.

INTRODUCTION: Thalassemia trait (TT) and iron deficiency anemia (IDA) are two common types of microcytic hypochromic anemia (MHA), but current diagnos...

Predicting long-term kidney graft failure using novel multi-omic blood-based biomarkers and artificial intelligence tools.

Kidney transplantation (KT) remains the preferred treatment for end-stage renal disease. With advancements in immunosuppressive regimens and KT survei...

Development and validation of a modified SOFA score for mortality prediction in candidemia patients.

Candidemia is a life-threatening bloodstream infection associated with high mortality rates, particularly in critically ill patients. Accurate risk st...

Cuff-less blood pressure monitoring via PPG signals using a hybrid CNN-BiLSTM deep learning model with attention mechanism.

Blood pressure (BP) serves as a fundamental indicator of cardiovascular health, measuring the force exerted by circulating blood against arterial wall...

Different prefrontal cortex activity patterns in bipolar and unipolar depression during verbal fluency tasks based on functional near infrared spectroscopy study.

This study aimed to investigate the functionality of the prefrontal cortex in patients with unipolar depression (UD) and bipolar depression (BD) using...

Spatial patterns of hepatocyte glucose flux revealed by stable isotope tracing and multi-scale microscopy.

Metabolic homeostasis requires engagement of catabolic and anabolic pathways consuming nutrients that generate and consume energy and biomass. Our cur...

Human protein interaction networks of ancestral and variant SARS-CoV-2 in organ-specific cells and bodily fluids.

Understanding SARS-CoV-2 human protein-protein interactions (PPIs) and the host response to infection is essential for developing effective COVID-19 a...

A supervised machine learning approach with feature selection for sex-specific biomarker prediction.

Biomarkers are crucial in aiding in disease diagnosis, prognosis, and treatment selection. Machine learning (ML) has emerged as an effective tool for ...

A modified generative adversarial networks method for assisting the diagnosis of deep venous thrombosis complications in stroke patients.

Discriminate deep vein thrombosis, one of the complications in early stroke patients, in order to assist in diagnosis. We have constructed a new metho...

Artificial intelligence-driven label-free detection of chronic myeloid leukemia cells using ghost cytometry.

Early diagnosis and treatment initiation of chronic myeloid leukemia (CML) are considered to increase the rate of deep molecular response. However, th...

Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms.

This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction mod...

Decoding the diagnostic biomarkers of mitochondrial dysfunction related gene variants in pediatric T cell acute lymphoblastic leukemia.

Mitochondrial dysfunction is crucial in the pathogenesis and drug resistance of pediatric T-cell acute lymphoblastic leukemia (T-ALL), a malignant hem...

A novel UHPLC-HRMS method for simultaneous determination of 20 amino metabolites and proteins in lymphoma patients' cells and serum.

Highly sensitive and selective monitoring of amino metabolites such as glutamine, arginine, tryptophan and related proteins played significant roles i...