Myeloma

Zero echo time MRI with deep learning reconstruction and chemical shift correction for detecting osteolytic myeloma lesions.

OBJECTIVE: We compared three magnetic resonance imaging (MRI) sequences-native zero echo time (ZTE), deep learning (DL)-chemical shift correction (CSC) reconstructed ZTE (ZTE-DLCSC), and gradient-echo black bone (BB)-in detecting osteolytic multiple myeloma (MM) lesions, using computed tomography (CT) as reference. MATERIALS AND METHODS: Patients who had undergone whole-body 18F-fluorodeoxiglucose...

Development and early feasibility testing of machine-learning algorithms to non-invasively assess hemoglobin levels.

The HeMonitor study evaluated the feasibility and accuracy of non-invasive hemoglobin (Hb) assessment using image-based techniques and machine learning in patients with hematologic malignancies. A total of 367 patients with hematologic malignancies and 184 healśśthy donors were enrolled, with fingernail and eyelid photographs collected and analyzed using Light Gradient-Boosting Machine (LightGBM) ...

Early Prediction of Diabetic Macular Edema via Machine Learning Survival Analysis on Checkup Data.

PURPOSE: To predict the risk of diabetic macular edema (DME) onset and to identify features of the risk subgroups. DESIGN: Population-based observatio...

Clinical subtypes and prognosis of new-onset atrial fibrillation in critically ill patients.

BACKGROUND: New-onset atrial fibrillation (NOAF) is a common cardiovascular complication in critically ill patients and is consistently associated wit...

Advancing child health: forecasting the next great research achievements.

Advances in pediatric research have improved the lives of infants, children, adolescents, and society at large. A decade ago the American Academy of P...

Cyclosporine in hematological disorders: mechanisms, clinical practice and emerging advances.

Cyclosporine A (CsA) functions as a calcineurin inhibitor that perturbs T cell activation via calcineurin-nuclear factor of activated T cells (CaN-NFA...

Machine Learning Model Predicts Clinical Adverse Events of Small Molecule Kinase Inhibitors in Cancer Patients Using On-/Off-Target Engagement and Tissue Selectivity.

Adverse events (AEs) of small molecule kinase inhibitors (SMKIs) at therapeutic doses in cancer patients are largely unpredictable in phase I-III stud...

Whole-body reduced-dose dual-energy CT with deep learning image reconstruction for detection of osteolytic lesions in multiple myeloma.

INTRODUCTION: Deep learning image reconstruction (DLIR) has been incorporated into dual-energy CT (DECT) to improve image quality. However, its applic...

KIF23 in disease pathogenesis and its therapeutic and diagnostic potential.

BACKGROUND: Kinesin family member 23 (KIF23) is a microtubule-dependent motor protein essential for cytokinesis, organelle transport, and signaling pa...

Ontology-Aligned Representation of Adverse Events in a Multiple Myeloma Clinical Trial.

To explore the potential of ontologies in improving adverse event identification, we developed ontology-aligned representations of adverse events extr...

Circadian Deregulation in Multiple Myeloma: BMAL1/CLOCK Expression Patterns and Diagnostic Performance.

BACKGROUND: Circadian clock disruption has emerged as a relevant axis in cancer; however, the expression patterns and diagnostic relevance of BMAL1 an...

Few-shot learning-driven discovery of Lutein suppresses Th1-mediated inflammation via glucose metabolism.

The discovery of selective immunosuppressants for T cell-mediated diseases like Ulcerative Colitis (UC) is a significant challenge. While traditional ...

Predicting anemia treatment outcomes in maintenance hemodialysis patients using multiple machine learning models.

BACKGROUND: Based on machine learning prediction models, we explored the anemia treatment attainment of patients on maintenance hemodialysis (MHD) and...

Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence.

Risk stratification is an important tool in clinical decision-making, yet current approaches often fail to translate sophisticated survival analysis i...

The negative sigmoid loss for controlling false positive rate in osteolytic lesion segmentation.

Multiple Myeloma (MM) is a malignancy that is commonly associated with the development of osteolytic lesions. To support MM diagnosis, low-dose Comput...

Contemporary Trends in Calcium Levels and Related Clinical Conditions: A Two Decade Analysis of U.S. National Health and Nutritional Examination Survey Data.

Ambulatory hypercalcemia is a proxy for primary hyperparathyroidism. Reports of an increased incidence of hypercalcemia and undiagnosed primary hyperp...

Machine learning models for predicting childhood anemia in Mozambique: analysis from national survey data.

A deep dive into ferritin nanoparticle advancements: experimental and computational perspectives.

Ferritin, a natural iron-storage protein, has emerged as a versatile platform in nanotechnology and biomedicine due to its biocompatible 12 nm nanocag...

Bridging In Silico Design and Experimental Validation: Virtual Screening and In Vitro Assessment of Biomimetic Anticancer Peptides Inspired by Horseshoe Crab Hemolymph Proteins.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, necessitating the development of more selective and safer the...

Contextualizing the Patient Blood Management (PBM) Program Through the Lens of Bioethical Principles.

In an increasingly specialized medical landscape, Patient Blood Management (PBM) has emerged as a multidisciplinary and ethically grounded approach to...