Hemophilia

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.

BACKGROUND: Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Knowledge gaps include whether molecular endotypes of psoriasis underlie distinct clinical phenotypes and the positive and negative molecular regulators of disease severity across tissue compartments. METHODS: We performed comprehensive RNA ...

Machine Learning-Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2- Early Breast Cancer Using Real-World and NATALEE Data.

PURPOSE: Despite current standard-of-care endocrine therapy, distant recurrence remains a concern for patients with hormone receptor-positive (HR+)/HER2- early breast cancer (EBC). Understanding individual recurrence risk would aid in clinical decision-making. We used machine learning to identify risk factors and develop recurrence risk prediction models. EXPERIMENTAL DESIGN: Predictor variables w...

Exploring structural diversity and dynamic stability of small-molecule PRMT5 inhibitors through machine learning-based QSAR and molecular modelling.

Protein arginine methyltransferase 5 (PRMT5) is a key epigenetic enzyme that catalyses symmetric arginine methylation on histone and non-histone prote...

Gut dysbiosis in oncology: a risk factor for immunoresistance.

The gut microbiome is recognized as a determinant of response to immune checkpoint inhibitor (ICI) therapies in cancer. However, the clinical translat...

NEIL1 suppresses ROS accumulation to promote temozolomide resistance and malignant progression in glioma cells.

BACKGROUND: Glioma is the most common malignant primary brain tumor. Temozolomide (TMZ) is the standard first-line chemotherapy, but its efficacy is s...

Reconnecting new tetrahydrobenzothieno-4-pyrimidine amides as potent PIM-1 kinase inhibitors via an integrated ligand, machine learning model and structure based scaffold hopping: In vitro anti-tumor investigations.

PIM1 has been known to be one of the prolific molecular target in the discovery of new potential anticancer drugs due to its engagement in activation ...

Investigating the mechanism of berberine in inhibiting periodontitis progression using machine learning, molecular docking, and experimental validation.

BACKGROUND: Periodontitis is a chronic inflammatory disease characterized by the destruction of periodontal tissues. Berberine (BBR), a natural alkalo...

Computer-Aided optimization of quinazoline-based Aurora kinase inhibitors for enhanced metabolic stability.

The quinazoline-based Aurora kinase inhibitor 9h, previously developed by our group, exhibited potent efficacy both in vitro and in vivo. However, its...

Strategy and efficiency-redefined discovery of novel nanomolar tyrosinase inhibitors: AI de novo molecular generation + expert-guided structural optimization.

Artificial intelligence (AI) has played an excellent supporting role in novel drug discovery and development. This study introduces a reinforcement le...

A chalcone-rich extract from licorice root inhibits liver steatosis and fibrosis via regulating PPARα and TGFβ signaling.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a major liver disorder, which seriously affects human health globally....

A novel polypeptide molecule attenuates atopic dermatitis by targeting CCR8-CCL1 axis.

Atopic dermatitis (AD) is a common chronic inflammatory skin disease with diverse clinical phenotypes. Although a variety of drugs are available, the ...

Evaluating Retrieval-Augmented Generation-Large Language Models for Infective Endocarditis Prophylaxis: Clinical Accuracy and Efficiency.

INTRODUCTION AND AIMS: The use of large language models (LLMs) in healthcare is expanding. Retrieval-augmented generation (RAG) addresses key LLM limi...

DSSA-PPI: enhancing binding affinity change prediction upon protein mutations using disentangled structure-sequence aware attention.

Accurate assessment of the effects of mutations on protein-protein interactions (PPIs) is crucial for understanding disease pathogenesis and the devel...

Machine learning-guided repurposing of FDA-approved quinolones as dual cholinesterase inhibitors: A multi-level docking, molecular dynamics, DFT, and SHAP-based analysis.

Alzheimer's disease (AD) involves progressive cholinergic degeneration, with acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) playing key ...

PD-L1 expression combined with immune phenotype is a potential predictor of outcome with PD-1 inhibitor monotherapy in patients with recurrent or metastatic head and neck squamous cell carcinoma.

OBJECTIVE: Programmed cell death-ligand 1 (PD-L1) expression and immune phenotype (IP) are potential predictive biomarkers for immune checkpoint inhib...

CoREST complex inhibition alters RNA splicing to promote neoantigen expression and enhance tumor immunity.

Epigenetic macromolecular enzyme complexes tightly regulate gene expression at the chromatin level and have recently been found to colocalize with RNA...

Targeting the DYNLL2-PAK1 axis inhibits caspase-11-dependent pyroptosis to alleviate sepsis.

Sepsis, a life-threatening syndrome caused by dysregulated host responses to infection, lacks effective therapeutic strategies due to its complex immu...

Precision treatment with artificial intelligence assisted subtyping enhances therapeutic efficacy in HR+/HER2- breast cancer: The LINUXtrial.

We report the results of LINUX (NCT05594095), a multicenter, randomized, controlled phase II platform trial aiming to identify effective precision tre...

Developing a Statistical Modeling-Based Machine Learning Approach for Capturing Drug Dosing Using a Proton Pump Inhibitor Case.

OBJECTIVE: To develop a statistical model to capture medication dosing for proton pump inhibitors (PPIs) using structured data from electronic health ...

Machine Learning-Based Prediction of Postoperative Vasoplegia After Cardiac Surgery.

OBJECTIVE: To develop and internally validate a machine-learning model for the early prediction of postoperative vasoplegia after cardiac surgery. DES...