Hematology

Latest AI and machine learning research in hematology for healthcare professionals.

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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 h...

Vascular segmentation of functional ultrasound images using deep learning.

Segmentation of medical images is a fundamental task with numerous applications. While MRI, CT, and ...

Machine reading and recovery of colors for hemoglobin-related bioassays and bioimaging.

Despite advances in machine learning and computer vision for biomedical imaging, machine reading and...

Advancing blood cell detection and classification: performance evaluation of modern deep learning models.

The detection and classification of blood cells are important in diagnosing and monitoring a variety...

From Molecular Precision to Clinical Practice: A Comprehensive Review of Bispecific and Trispecific Antibodies in Hematologic Malignancies.

Multispecific antibodies have redefined the immunotherapeutic landscape in hematologic malignancies....

Tumor-specific draining lymph node CD8 T cells orchestrate an anti-tumor response to neoadjuvant PD-1 immune checkpoint blockade.

Elucidating the anti-tumor role of tumor-draining lymph nodes (tdLNs) in patients could offer critic...

Integration of Metabolomics and Proteomics Reveals the Molecular Characterization of High-Altitude Hyperuricemia.

Hypobaric hypoxia-induced hyperuricemia (HUA) is a major health challenge for high-altitude populati...

Incorporating the STOP-BANG questionnaire improves prediction of cardiovascular events during hospitalization after myocardial infarction.

Obstructive sleep apnea (OSA) may impact outcomes in acute coronary syndrome (ACS) patients. The Glo...

One-Drop Serum Screening Test to Monitor Tissue Iron Accumulation.

Although iron is an essential element for vital body functions, iron overload (IO) is accompanied by...

Predicting the Higher Energy Need for Effective Defibrillation Using Machine Learning Based on an Animal Model.

: Early defibrillation improves outcomes in cardiac arrest, but the optimal defibrillation strategy ...

A Mixed-attention Network for Automated Interventricular Septum Segmentation in Bright-blood Myocardial T2* MRI Relaxometry in Thalassemia.

RATIONALE AND OBJECTIVES: This study develops a deep-learning method for automatic segmentation of t...

RhDnostics: A Machine Learning-Based Predictive Algorithm Model for RhD-Negative and DEL Blood Group Screening.

BACKGROUND: The D-elution (DEL) phenotype is serologically mislabeled as Rh-negative because of the ...

Cell death-related signature genes: risk-predictive biomarkers and potential therapeutic targets in severe sepsis.

Sepsis is a systemic inflammatory response syndrome that predisposes to severe lung infections (SeAL...

Integrative single-cell and cell-free plasma RNA transcriptomics identifies biomarkers for early non-invasive AD screening.

INTRODUCTION: Data-driven omics approaches have rapidly advanced our understanding of the molecular ...

Predictive modeling of hemoglobin refractive index using Gaussian process regression with interpretability through partial dependence plots.

Accurately predicting the refractive index of hemoglobin across various wavelengths and concentratio...

Divergent Immune Pathways in Coronary Artery Disease and Aortic Stenosis: The Role of Chronic Inflammation and Senescence.

Coronary artery disease (CAD) remains a major cause of cardiovascular morbidity and mortality, with ...

A cell-interacting and multi-correcting method for automatic circulating tumor cells detection.

Sensitive detection of circulating tumor cells (CTCs) from peripheral blood can serve as an effectiv...

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