Hematology

Preoperative Factors Associated With In-Hospital Major Bleeding After Percutaneous Coronary Intervention: A Systematic Review.

BACKGROUND: Preoperative risk assessment of bleeding after percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and, most importantly, for clinical decision-making. This systematic review aims to summarise preoperative factors associated with post-PCI in-hospital major bleeding.

Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence.

The integration of artificial intelligence (AI) into medical diagnostics is transforming the landscape of healthcare, particularly in hematology. AI technologies, leveraging advanced machine learning algorithms and big data analytics, are revolutionizing the diagnosis of hematological disorders such as anemia, leukemia, and lymphoma. This review explores how AI enhances diagnostic accuracy, effici...

Shengxuebao Mixture improves carboplatin-induced anemia by inhibiting apoptosis and ferroptosis.

ETHNOPHARMACOLOGICAL RELEVANCE: Shengxuebao Mixture (SXB) is a traditional Chinese medicine which has been widely used on treating Chemotherapy-induce...

Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children.

Disseminated intravascular coagulation (DIC) is a thrombo-hemorrhagic disorder that can be life-threatening in critically ill children, and the quest ...

An Early Thyroid Screening Model Based on Transformer and Secondary Transfer Learning for Chest and Thyroid CT Images.

IntroductionThyroid cancer is a common malignant tumor, and early diagnosis and timely treatment are crucial to improve patient prognosis. With the in...

Natural language processing for identifying major bleeding risk in hospitalised medical patients.

BACKGROUND: Major bleeding is a severe complication in critically ill medical patients, resulting in significant morbidity, mortality, and healthcare ...

A deep-learning algorithm (AIFORIA) for classification of hematopoietic cells in bone marrow aspirate smears based on nine cell classes-a feasible approach for routine screening?

Bone marrow cytology plays a key role for the diagnosis and classification of hematological disease and is often the first step in the acute setting o...

O blood usage trends in the pediatric population 2015-2019: A multi-institutional analysis.

BACKGROUND: In 2019, AABB released the bulletin "Recommendations on the Use of Group O Red Blood Cells" in which the recommendations about pediatric a...

Gold nanorods as multidimensional optical nanomaterials: machine learning-enhanced quantitative fingerprinting of proteins for diagnostic applications.

The rapid and precise quantification and identification of proteins as key diagnostic biomarkers hold significant promise in allergy testing, disease ...

The interpretable machine learning model for depression associated with heavy metals via EMR mining method.

Limited research exists on the association between depression and heavy metal exposure. This study aims to develop an interpretable and efficient mach...

Addressing Hemolysis-Induced Loss of Sensitivity in Lateral Flow Assays of Blood Samples with Platinum-Coated Gold Nanoparticles and Machine Learning.

Gold nanoparticles (GNPs), which appear red, are widely used as labels in lateral flow assays (LFAs) for visual detection. However, in blood-derived s...

A prospective study for the examination of peripheral blood smear samples in pediatric population using artificial intelligence.

BACKGROUND/AIM: Peripheral blood smear (PBS) and bone marrow aspiration are gold standards of manual microscopy diagnostics for blood cell disorders. ...

Development and Validation of Models for Preoperative Prediction of Risk and Postoperative Detection of Noninfectious Complications Using Interpretable Machine Learning and Electronic Health Record Data.

OBJECTIVE: To apply interpretable machine learning methodology to electronic health record data to develop models for preoperative risk estimation and...

Discovery and characterization of novel FAK inhibitors for breast cancer therapy via hybrid virtual screening, biological evaluation and molecular dynamics simulations.

Focal adhesion kinase (FAK) is a critical drug target implicated in various disease pathways, including hematological malignancies and breast cancer. ...

Machine learning-based prognostic model for bloodstream infections in hematological malignancies using Th1/Th2 cytokines.

OBJECTIVE: Bloodstream infection (BSI) is a significant cause of mortality in patients with hematologic malignancies(HMs), particularly amid rising an...

Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis.

: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are...

Nutritional predictors of lymphatic filariasis progression: Insights from a machine learning approach.

Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes disfiguring of the affected extremities, often leading to permane...

Traumatic brain injury: Bridging pathophysiological insights and precision treatment strategies.

Blood-brain barrier disruption and the neuroinflammatory response are significant pathological features that critically influence disease progression ...

Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study.

Preterm birth (PTB), defined as delivery before 37 weeks, affects 15 million infants annually, accounting for 11% of live births and over 35% of neona...

Deep learning image analysis for continuous single-cell imaging of dynamic processes in Plasmodium falciparum-infected erythrocytes.

Continuous high-resolution imaging of the disease-mediating blood stages of the human malaria parasite Plasmodium falciparum faces challenges due to p...