Leukemia

An interpretable machine learning model integrating peripheral blood lncRNAs and clinical variables for phase classification in chronic myeloid leukemia.

BACKGROUND: Accurate disease phase assessment remains clinically important in chronic myeloid leukemia (CML), as progression to accelerated or blast phase (AP/BP) is associated with therapeutic resistance and poor prognosis. We aimed to develop an interpretable machine learning (ML) framework integrating peripheral blood long non-coding RNA (lncRNA) expression and clinical variables for disease ph...

Deep learning-based effusion-synovitis volume measured on MRI is associated with osteoarthritis progression: A longitudinal analysis of data from the osteoarthritis initiative.

OBJECTIVE: To investigate whether the 48-month change in effusion-synovitis volume (ΔESV) is associated with concurrent knee osteoarthritis progression, and to compare these associations with those of semiquantitative change in effusion-synovitis using the MRI Osteoarthritis Knee Score (ΔMOAKS). DESIGN: In this study using data from the Osteoarthritis Initiative conducted from 02/2004-10/2015, dee...

Predicted protein-protein interactions between sugar beet root maggot trypsins and sugar beet Kunitz trypsin inhibitors using deep learning.

Sugar beet (Beta vulgaris ssp. vulgaris), SB, is one of two plants from which sugar is broadly extracted, annually accounting for 55% of U.S. sugar ($...

AI-assisted molecular docking and molecular dynamics simulations for predicting off-target effects of AKT1 ATP-competitive inhibitors.

AKT1 is a member of the AGC kinase family and represents a key therapeutic target in cancer. Although ATP-competitive compounds can act as potent inhi...

Immunosuppressive regimens and long-term kidney transplant outcomes: A dual survival modeling framework.

Optimizing immunosuppressive therapy remains central to improving long-term outcomes after kidney transplantation. Both induction and maintenance ther...

Integrated single-cell RNA sequencing and Bulk-RNA technologies reveal the immunological characteristics of lactylation related-genes in glioblastoma.

OBJECTIVE: Glioblastoma (GBM) is the most aggressive type of intracranial malignant tumor, known for its extremely poor prognosis. Lactylation, a newl...

AI-driven discovery of GPNMB CAR T cells as a multi-cancer therapy.

Chimeric antigen receptor (CAR) T cells have demonstrated curative potential in hematologic cancers and increasing efficacy in solid tumors and non-ma...

Dynamic gut microbiota shift in chronic liver disease progression: machine learning models and the E/Er ratio as a predictive biomarker.

BACKGROUND: Chronic liver disease (CLD) and its progression to compensated cirrhosis (CC), decompensated cirrhosis (DC), and acute-on-chronic liver fa...

Bridging the rural logistics penalty: quantifying structural costs and designing policy mechanisms for home TCM delivery to older adults with chronic respiratory diseases.

BACKGROUND: Equitable access to prescribed therapies remains challenging for older adults with chronic respiratory diseases (CRDs) in rural China. Liq...

Is cellular senescence a biological feature of Long COVID? A transcriptomic analysis across comparative post-acute sequelae phenotypes.

INTRODUCTION: Cellular senescence, involving cell-cycle arrest and inflammatory factor release, may play a role in Long COVID development. We investig...

Quantitative Detection of Entotic Cell-In-Cell Structures Using Deformable Segmentation and Deep Learning.

Cell-in-cell (CIC) structures are among the most intriguing cellular phenomena occasionally observed in human cancer specimens. Once regarded as incid...

Machine learning-based integration develops a novel lysosome-related prognostic signature associated with prognosis and immune infiltration landscape in acute myeloid leukemia.

BACKGROUND: Lysosomes are essential for intracellular degradation and recycling, and changes in their function significantly contribute to tumor growt...

Identification and validation of BLK and OSBPL10 as diagnostic and prognostic biomarkers for nasopharyngeal carcinoma through machine learning algorithms.

Despite substantial advances in radiotherapy and chemotherapy for nasopharyngeal carcinoma (NPC), a subset of patients still develops metastasis or re...

Real-World Application of a Machine Learning-Based Early Recurrence Model for Guiding Adjuvant Chemotherapy Use in Patients with Gallbladder Cancer.

BACKGROUND/AIMS: Although recent randomized controlled trials have reported the efficacy of adjuvant chemotherapy for resected biliary tract cancer, d...

Integrating experimental biology, computational methods, and artificial intelligence in anticancer drug discovery: Bridging the translational gap.

Cancer remains a leading cause of mortality globally, with the incidence projected to reach 28.4 million new cases annually by 2040. Traditional drug ...

Predicting Renal Tumor Pathology from Intraoperative Gross Appearance: An AI-Based Pilot Study.

OBJECTIVE: To evaluate the feasibility of using convolutional neural networks (CNNs) and vision transformers (ViTs) to predict renal tumor pathology i...

Machine learning-guided risk stratification in elderly AML based on genomic, immunophenotypic and therapeutic profiles.

BACKGROUND: Elderly patients with acute myeloid leukemia (AML) exhibit considerable biological and clinical heterogeneity, hindering precise prognosis...

Next Generation Digital Morphology: Blast Preclassification in Bone Marrow Aspirates.

INTRODUCTION: Morphologic evaluation of peripheral blood (PB) smears and bone marrow aspirates (BMA) remains central to the diagnosis of acute leukemi...

Integrated network toxicology and experimental validation to investigate potential mechanisms associated with aspartame-induced malignant phenotypic changes in colorectal cancer.

To investigate the potential molecular mechanisms underlying aspartame (APM)-induced malignant phenotypic changes in colorectal cancer (CRC). Candidat...

DeepDisSNP: Predicting disease-associated SNPs by representation learning on disease and SNP linkage disequilibrium networks.

Genome-wide association studies (GWAS) have identified numerous disease-associated single nucleotide polymorphisms (SNPs), yet many potential disease-...