Leukemia

Epigenetic-Metabolic interplay in chronic kidney disease mortality: insights from grimage acceleration and transcriptomic profiling.

BACKGROUND: Chronic kidney disease (CKD) is associated with a substantially elevated risk of mortality. Although GrimAge acceleration (GAA) and metabolic syndrome (MetS) are both implicated in this risk, their combined impact and the underlying biological mechanisms remain poorly understood. METHODS: This study integrated data from National Health and Nutrition Examination Survey (NHANES; n = 2,52...

Beyond target lesions: Prognostic value of longitudinal AI-derived whole-body [1⁸F]FDG PET/CT metrics in metastatic melanoma.

PURPOSE: To investigate the prognostic value of an artificial intelligence (AI)-based semi-automated tool for longitudinal whole-body quantification of total metabolic tumor volume (TMTV) and total lesion glycolysis (TLG) on [1⁸F]FDG PET/CT in patients with metastatic melanoma undergoing immune checkpoint inhibitor (ICI) therapy, and to assess its prognostic relevance alongside established PET-bas...

DREAMER-S: Deep leaRning-Enabled Attention-based Multiple-instance approaches with Explainable Representations for Spatial biology.

Identifying image features that associate strongly with diagnostic or prognostic classes in large-scale, multi-channel spatial imaging is challenging ...

Multicentre prospective cohort study to develop and validate a machine learning-based model for predicting 6-month all-cause mortality in elderly patients with advanced chronic obstructive pulmonary disease in China: study protocol.

INTRODUCTION: Chronic obstructive pulmonary disease (COPD) has an unpredictable clinical course, causing difficulties in short-term mortality predicti...

Artificial intelligence-enhancement of flow cytometry data accelerates the identification of measurable residual chronic lymphocytic leukemia.

Flow cytometry (FC) is essential for detecting measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL), but its use is limited by the ...

Employing network toxicology, molecular docking, machine learning, and single-cell analysis to analyze BPA exposure-induced ccRCC.

This study investigates the molecular mechanisms underlying bisphenol A (BPA)-induced clear cell renal cell carcinoma (ccRCC). We integrated transcrip...

Generating Labeled Low-Heterogeneity Transcriptomes Using CRISPRa and CRISPRi Can Improve Phenotype Prediction by Deep Learning.

The development of deep learning (DL) methods in biology holds great promise for fundamental knowledge, biomedicine, and agriculture. The most global ...

Integrative transcriptomic and single-cell analysis reveals metabolic reprogramming and hexosamine biosynthesis-mediated tumor microenvironment remodeling in prostate cancer.

BACKGROUND: Prostate cancer is one of the most common malignancies in men and exhibits substantial clinical heterogeneity. A considerable proportion o...

Optimizing Deep Learning for Renal Mass Characterization in Challenging Cases: A Comparative Study of Spatial-Input Strategies and a Validated ROI-Only DLR Nomogram.

PURPOSE: Our purpose was to develop and validate an integrated clinical deep learning radiomics (DLR) nomogram for differentiation of fat-poor angiomy...

TwinRL-Onco: A World Model-Empowered Digital Twin Framework with Hierarchical Reinforcement Learning for Venetoclax Resistance Trajectory Prediction and Adaptive Therapy Optimization in Chronic Lymphocytic Leukemia.

Chronic lymphocytic leukaemia (CLL) presents considerable therapeutic obstacles due to the development of treatment-resistant disease, especially with...

Frailty and Fitness in Older Adults With Acute Myeloid Leukemia: From Chronological Age to Multiparametric Assessment.

Acute Myeloid Leukemia predominantly affects older adults, who often present with comorbidities, functional impairment, and frailty, limiting eligibil...

Explainable machine learning-guided integrated multiomics analysis reveals macrophage-driven immune suppression in breast cancer.

Despite thorough characterizations of cellular compositions within the breast tumor microenvironment (TME), their implications for disease progression...

In silico discovery of novel small-molecule PD-L1 inhibitors through a multi-stage computational workflow integrating machine learning and molecular dynamics.

Cancer immunotherapy targeting the PD-1/PD-L1 pathway has transformed modern oncology; however, developing small-molecule inhibitors as viable alterna...

Predicting response to neoadjuvant chemotherapy combined with immunotherapy in gastric cancer based on habitat imaging and peritumoral radiomics: a two-center study.

BACKGROUND: Predicting pathological response to neoadjuvant chemotherapy combined with immunotherapy (NACI) in locally advanced gastric cancer (LAGC) ...

Machine learning-based prediction of 3-month mortality in elderly patients with non-small cell lung cancer and bone metastases.

Elderly patients with non-small cell lung cancer (NSCLC) and bone metastases face a dire prognosis, creating an urgent need for accurate short-term mo...

Computational Advances in Biomaterial Engineering: Mapping Research Trajectories Through Bibliometric Analysis.

Alternatives to animal models, including computational-based approaches, are now prioritized by regulatory and funding agencies in biomedical research...

Molecular and spatial differences in the tumor microenvironment of high-grade serous ovarian cancers with short versus long-term survival.

OBJECTIVE: Survival of patients diagnosed with advanced-stage high-grade serous ovarian cancer (HGSOC) varies widely. Understanding the biological and...

Multi-region proteomic mapping identifies FTL1 and SERPINA3K as protective factors in cardiac aging.

Aging is a well-recognized risk factor in cardiovascular diseases (CVDs), primarily due to its association with the gradual decline in cardiac functio...

Estimating temporal treatment-effect patterns of radiotherapy and chemotherapy in lower-grade gliomas using causal machine learning.

Treatment decisions for lower-grade gliomas (WHO grades 2-3) rest on trial averages, which lack temporal resolution. We applied Causal Analysis of Sur...

Integrative single-cell and bulk transcriptomic analysis identifies lesion-associated gene signatures for prognostic stratification and therapeutic guidance in head and neck squamous cell carcinoma.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor prognosis and variable response to therapy. E...