Myeloma

Machine learning-assisted prognosis of multiple myeloma side population cells via SRGs and OCLR stemness index.

BACKGROUND AND OBJECTIVE: Relapse in Multiple Myeloma, driven by therapy-resistant cancer stem cells, necessitates the development of more specific and accurate prognostic models. Existing stemness indices often lack specificity for the unique biology of Multiple Myeloma. This study aimed to develop, validate, and optimize a novel prognostic gene signature derived from Side Population cells, a wel...

DGPCNet: anemia detection from multimodal palm, fingernails, and eye conjunctiva images using deep global pyramid convolutional network.

BACKGROUND: Anemia is one of the most common hematological disorders causing fatigue, increased vulnerability to infection, low birth weight, and threatening the physical and mental development of children. Hence, the early identification reduces the risk of patients and facilitates early intervention. On the other hand, the blood collection of the adults is simple, and the results will be precise...

The Application of Machine-Learning Algorithms for Multiclass Classification of Microcytic Anemia Revealed That a Minimum Required Number of Hematological Parameters Is Enough to Achieve High Diagnostic Accuracy.

INTRODUCTION: Thalassemia is a major global health concern, as declared by the World Health Organization (WHO). Accurate screening of heterozygotes is...

Advancing Anemia Detection With Deep Neural Networks: A Comparative Analysis of Training Strategies Using Conjunctival Images.

AI-Quantified ¹¹C-MET PET/CT bone marrow metabolic activity for prognostic assessment in newly diagnosed multiple myeloma.

PURPOSE: To develop and validate an AI method for automated quantification of whole-skeleton bone marrow (BM) metabolic activity using Carbon 11 (11C)...

High-precision wavefield simulation and deep learning-based sound speed reconstruction for transcranial ultrasound imaging.

Transcranial ultrasound imaging plays an important role in the diagnosis of brain diseases and the monitoring of brain function. However, the quality ...

Machine Learning-Based Prediction of β-Thalassemia Trait Using Red Blood Cell Indices.

OBJECTIVES: Anemia is a significant global health concern, with hypochromic microcytic anemia being the most common type. Among its causes, β-thalasse...

Precision Nutritional Genomics, Gut Microbiota and Artificial Intelligence in Chronic Kidney Disease.

Chronic kidney disease (CKD) is a prevalent global health issue, and nutritional management of CKD is an integral component through all stages of the ...

Validation of novel low-dose CT methods for quantifying bone marrow in the appendicular skeleton of patients with multiple myeloma: initial results from the [18F]FDG PET/CT sub-study of the Phase 3 GMMG-HD7 Trial.

PURPOSE: The clinical significance of medullary abnormalities in the appendicular skeleton detected by computed tomography (CT) in patients with multi...

Development of an interpretable machine learning model to predict short-term bleeding risk in patients receiving dual antithrombotic therapy following cardiac surgery.

INTRODUCTION: Bleeding is a serious complication in cardiac surgery, especially among patients receiving combined anticoagulant and antiplatelet thera...

B4GALT3 as a Key Glycosyltransferase Gene in Multiple Myeloma Progression: Insights From Bioinformatics, Machine Learning, and Experimental Validation.

Glycosylation abnormalities are critical in the progression of various cancers. However, their role in the onset and prognosis of multiple myeloma (MM...

Predictors of frontline doublet or triplet regimen initiation in transplant-ineligible newly diagnosed multiple myeloma.

AIMS: To identify predictors for initiation of frontline doublet versus triplet therapy for transplant-ineligible newly diagnosed multiple myeloma.

A comprehensive targeted panel of 295 genes: Unveiling key disease initiating and transformative biomarkers in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a hematological malignancy that progresses from a benign precursor stage known as Monoclonal Gammopathy of Undete...

Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers.

PANoptosis is closely associated with tumorigenesis and therapeutic response, yet its role in multiple myeloma (MM) remains unclear. This study analyz...

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...

Can we identify individuals at risk to develop multiple myeloma? A machine learning-based predictive model.

Multiple myeloma evolves unnoticed over years, and when diagnosed, organ damage is common. Electronic health records (EHR) can help in developing pred...

Advanced molecular approaches to thalassemia disorder and the selection of molecular-level diagnostic testing in resource-limited settings.

Beta-thalassemia is a genetic disorder that significantly burdens healthcare systems globally. This inherited blood disorder, categorized into beta-th...

Assessing serum thrombopoietin for enhanced diagnosis of ITP, AA, and MDS using machine learning: A retrospective cohort study.

Differentiating between immune thrombocytopenia (ITP), aplastic anemia (AA), and myelodysplastic syndromes (MDS) is critical due to the distinct treat...

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 of blood-related illnesses, such as anemia, leuke...

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. Bispecific antibodies (BsAbs), which redirect cyt...