Leukemia

Multiscale analysis and optimal glioma therapeutic candidate discovery using the CANDO platform.

Glioma is a highly malignant brain tumor with limited treatment options. We employed the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery to predict new glioma therapies. We began by computing interaction scores between extensive libraries of drugs/compounds and proteins to generate "interaction signatures" that model compound behavior on a p...

[Expert consensus on optimizing the multidisciplinary clinical pathways and management models for pulmonary function testing].

The promotion and application of pulmonary function tests (PFTs) in China have achieved preliminary success;however, numerous deficiencies persist in multidisciplinary PFT clinical application pathways and management. Current challenges remain in identifing target population, implementing pathways within and outside medical institutions, allocating hardware, training personnel, managing informatio...

CMML2AML: machine-learning discovery of co-mutations and specific single mutations predictive of blast transformation in chronic myelomonocytic leukemia.

Contemporary risk models in chronic myelomonocytic leukemia (CMML) focus on the prognostic relevance of individual rather than concurrent mutations. I...

Deep learning analysis of soybean cyst nematode effectors to proven soybean resistance genes and homolog identification in the sugar beet-sugar beet root maggot plant pathosystem.

Sugar beet (Beta vulgaris ssp. vulgaris) is one of two plants from which sugar is broadly produced, accounting for 55% of U.S. sugar ($1B, U.S.) and 3...

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genom...

Clinical relevance of the characterisation and quantification of CD8+ T-cell infiltration in high-grade serous ovarian carcinoma using machine learning-based image analysis of dual immunostaining.

INTRODUCTION: High-grade serous carcinoma (HGSC) is the most common and aggressive subtype of ovarian cancer. Although tumour stage and complete cytor...

Quantitative CT Perfusion as a prognostic biomarker for chemotherapy response in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: Response evaluation of pancreatic ductal adenocarcinoma (PDAC) with routine contrast-enhanced CT (CECT) using RECIST is currently inadequa...

Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.

The existing TNM staging system provides insufficient prognostic information in gastric cancer (GC) patients. This study aims to establish a pathomics...

The Effect of Glenoid Rotational Malalignment on Best-Fit Circles Based on AI-Generated Mathematically True Glenoid En-Face Views.

BACKGROUND: The majority of studies that measure glenoid bone loss in the context of shoulder instability, are based on a sagittal image, termed the '...

Predicting hepatocellular carcinoma in people with hepatitis B: a comparison between Cox proportional hazard and machine learning models.

BACKGROUND AND AIM: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with chronic hepatitis B virus (HBV) infectio...

Machine learning-based programmed cell death gene signature for prognosis and drug sensitivity in breast cancer.

Breast cancer (BRCA) heterogeneity necessitates robust prognostic biomarkers. Programmed cell death (PCD) serves a key role in tumor progression and t...

Artificial Intelligence in Personalized Breast Cancer Drug Safety: From Preclinical Toxicology to Clinical Risk Management.

PURPOSE: The artificial intelligence (AI) implementation in personalized medicine has transformed drug safety, especially in breast cancer treatment. ...

Deep learning for adaptive chemotherapy: A DDPG-based approach to optimizing tumor-immune dynamics.

In this article, we propose a deep reinforcement learning based chemotherapy regulation framework to realize personalized and dynamic optimization of ...

Patch Uniform Fusion Transformer with circle-inspired optimized walrus-based feature selection for high-dimensional data analysis.

PURPOSE: Feature selection approaches have historically been limited by processing performance during high-dimensional data analysis, which presents a...

Integrated Strategies for Nursing and Rehabilitation in Lung Cancer Patients.

This review delineates the pivotal role of nursing and rehabilitation in perioperative management and chemotherapy support for lung cancer patients, w...

Artificial Intelligence-Enabled Prediction of Reversible Versus Irreversible Chemotherapy-Induced Myocardial Injury: Toward Precision Cardio-Oncology.

Cancer therapy-related cardiac dysfunction remains a major cause of morbidity among cancer survivors and may interrupt life-saving oncologic therapy o...

Targeted Real-Time Assessment of Chronic Pain (TRAC-Pain) in Youth: Protocol for a Digital Biosignature Development Through a Prospective Observational Cohort Study.

BACKGROUND: Approximately 1 in 5 children and adolescents live with chronic pain, with musculoskeletal (MSK) pain being one of the most prevalent subt...

Screening and classification of anti-angiogenic VEGFR2 inhibitors with supervised machine learning, deep learning and molecular docking and molecular dynamics simulation.

Vascular Endothelial Growth Factor Receptor 2 (VEGFR2) is a critical therapeutic target in cancer due to its role in pathological angiogenesis and tum...

Synthetic data-augmented machine learning for 30-day readmission prediction in patients with chronic conditions: a retrospective real-world study.

OBJECTIVES: To develop and evaluate an explainable machine learning framework enhanced with synthetic data generation to predict unplanned 30-day hosp...

Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response.

Immunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge....