Myeloma

Metabolomics and machine learning approaches for diagnostic biomarkers screening in systemic light chain amyloidosis.

Delayed diagnosis of systemic light chain (AL) amyloidosis is common and associated with worse survival and early mortality. Current diagnosis still relies on invasive tissue biopsies, highlighting the need for sensitive, noninvasive biomarkers for early diagnosis. This study aims to identify promising biomarkers for the early diagnosis of AL amyloidosis. Peripheral venous blood samples from 70 ne...

Bone-wise rigid registration of femur, tibia, and fibula for the tracking of temporal changes.

BACKGROUND: Multiple myeloma (MM) induces temporal alterations in bone structure, such as osteolytic bone lesions, which are challenging to identify through manual image interpretation. The large variation in radiologists' assessments, even at expert centers, further complicates diagnosis. Automatic image analysis methods, including segmentation and registration, can expedite detecting and trackin...

Inhibition of CDC27 O-GlcNAcylation coordinates the antitumor efficacy in multiple myeloma through the autophagy-lysosome pathway.

Multiple myeloma (MM) is a prevalent hematologic malignancy characterized by abnormal proliferation of cloned plasma cells. Given the aggressive natur...

Assessment of anemia recovery using peripheral blood smears by deep semi-supervised learning.

Monitoring anemia recovery is crucial for clinical intervention. Morphological assessment of red blood cells (RBCs) with peripheral blood smears (PBSs...

Ultrasensitive Detection of Circulating Plasma Cells Using Surface-Enhanced Raman Spectroscopy and Machine Learning for Multiple Myeloma Monitoring.

Multiple myeloma is a hematologic malignancy characterized by the proliferation of abnormal plasma cells in the bone marrow. Despite therapeutic advan...

Optimizing papermaking wastewater treatment by predicting effluent quality with node-level capsule graph neural networks.

Papermaking wastewater consists of a sizable amount of industrial wastewater; hence, real-time access to precise and trustworthy effluent indices is c...

Genomic determinants of biological age estimated by deep learning applied to retinal images.

With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from laten...

Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes.

As per world health organization, Anemia is a most prevalent blood disorder all over the world. Reduced number of Red Blood Cells or decrease in the n...

A machine learning tool for early identification of celiac disease autoimmunity.

Identifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagno...

Very low doses of rituximab in autoimmune hemolytic anemia-an open-label, phase II pilot trial.

INTRODUCTION: Although rituximab is approved for several autoimmune diseases, no formal dose finding studies have been conducted. The amount of CD20+ ...

Comparative transcriptomic and molecular biology analyses to explore potential immune responses to challenge in .

is a significant pathogen affecting shrimp and crab farming, particularly strains carrying genes associated with acute hepatopancreatic necrosis synd...

A potential predictive model based on machine learning and CPD parameters in elderly patients with aplastic anemia and myelodysplastic neoplasms.

BACKGROUND: Aplastic anemia (AA) and myelodysplastic neoplasms (MDS) have similar peripheral blood manifestations and are clinically characterized by ...

Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context.

BACKGROUND: Anemia during pregnancy is a significant public health concern, particularly in resource-limited settings. Machine learning (ML) offers pr...

Radiomics and Artificial Intelligence Landscape for [F]FDG PET/CT in Multiple Myeloma.

[F]FDG PET/CT is a powerful imaging modality of high performance in multiple myeloma (MM) and is considered the appropriate method for assessing treat...

AI in oral medicine: is the future already here? A literature review.

Objective Artificial intelligence (AI) is reshaping many healthcare disciplines, mainly with newly developed computer systems or machines that have th...

MultiThal-classifier, a machine learning-based multi-class model for thalassemia diagnosis and classification.

BACKGROUND: The differential diagnosis between iron deficiency anemia (IDA) and thalassemia trait (TT) remains a significant clinical challenge. This ...

Optimizing anemia management using artificial intelligence for patients undergoing hemodialysis.

Patients with end-stage kidney disease (ESKD) frequently experience anemia, and maintaining hemoglobin (Hb) levels within a targeted range using eryth...

HiDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data.

In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, when in r...

Deep Reinforcement Learning for personalized diagnostic decision pathways using Electronic Health Records: A comparative study on anemia and Systemic Lupus Erythematosus.

BACKGROUND: Clinical diagnoses are typically made by following a series of steps recommended by guidelines that are authored by colleges of experts. A...

Predicting malnutrition-based anemia in geriatric patients using machine learning methods.

BACKGROUND: Anemia due to malnutrition may develop as a result of iron, folate and vitamin B12 deficiencies. This situation poses a higher risk of mor...