Hemophilia

Development of a predictive model for eosinophilia in patients treated with ampicillin/sulbactam or piperacillin/tazobactam.

AIM: We aim to identify risk factors for antibiotic-induced eosinophilia in hospitalized patients receiving penicillin/beta-lactamase inhibitor therapy and to develop a machine learning-enhanced predictive risk-scoring model. METHODS: A retrospective cohort study was conducted involving 490 hospitalized patients treated with intravenous ampicillin/sulbactam or piperacillin/tazobactam. After applyi...

Deep learning and molecular dynamics reveal promising EZH2 inhibitors for epigenetic cancer targeting.

Fine-tuned REINVENT generative model integrated with structure- and ligand-based computational approaches was applied to identify novel EZH2 inhibitors. The generated molecules closely reproduced the structural and physicochemical property distributions of ChEMBL compounds, confirming their relevance to drug-like chemical space. Classification performance was robust (ROC-AUC ≥ 0.87 across training...

Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.

Chronic kidney disease (CKD) represents a major and expanding global health challenge, with prevalence rising due to aging populations, diabetes, hype...

Multiomic insights into the MPO-mediated NET formation pathway in alcohol-induced epilepsy risk.

Epilepsy is a highly prevalent chronic central nervous system disorder that imposes substantial societal and economic burdens. Inconsistent associatio...

Perioperative Acute Kidney Injury: What's New in 2025?

Perioperative acute kidney injury (AKI) is a frequent complication that affects outcomes well beyond the perioperative period. Its mechanisms remain i...

Leveraging the ADAM10 prodomain for selective inhibition to enhance recovery after myocardial infarction.

BACKGROUND AND PURPOSE: Inflammation plays a crucial role in the development and progression of numerous acute and chronic diseases such as myocardial...

Multiomics combined with machine learning defines unique molecular subtypes of cholangiocarcinoma and identifies TNK1 as a therapeutic target.

BACKGROUND AND AIMS: Cholangiocarcinoma (CCA) is one of the most lethal cancers, characterized by molecular heterogeneity and treatment resistance. To...

Nivolumab plus ipilimumab for potentially resectable hepatocellular carcinoma: Long-term efficacy and biomarker exploration.

BACKGROUND & AIMS: Immune checkpoint inhibitor-based combination therapy has demonstrated high objective response rates in patients with hepatocellula...

Discovery of natural RORγt inhibitor using machine learning, virtual screening, and in vivo validation.

INTRODUCTION: Retinoic acid receptor-related orphan receptor gamma t (RORγt) is a crucial transcription factor regulating Th17 cells, which secrete th...

Hot-Spot-Guided Generative Deep Learning for Drug-Like PPI Inhibitor Design.

Protein-protein interactions (PPIs) are essential therapeutic targets, yet their large and relatively flat interfaces hinder the development of small-...

Machine-Learning-Driven Discovery of -Phenylbenzenesulfonamides as a Novel Chemotype for Lactate Dehydrogenase A Inhibition with Anti-Pancreatic Cancer Activity.

Lactate dehydrogenase A (LDHA) is a promising target for cancer therapy due to its crucial role in aerobic glycolysis. Despite extensive efforts, the ...

AI based natural inhibitor targeting RPS20 for colorectal cancer treatment using integrated computational approaches.

The increasing global incidence of cancer emphasizes the vital role of machine learning algorithms and artificial intelligence (AI) in identifying nov...

Integrated Nanopore and short-read RNA sequencing identifies dysregulation of METTL3- m6A modifications in endocrine therapy- sensitive and resistant breast cancer cells.

The role of epitranscriptomic changes in the development of acquired endocrine therapy (ET)- resistance in estrogen receptor α (ER) expressing breast ...

Machine learning-based QSAR and structure-based virtual screening guided discovery of novel mIDH1 inhibitors from natural products.

Mutations in isocitrate dehydrogenase 1 (IDH1) have been widely observed in various tumors, such as gliomas and acute myeloid leukemia, and therefore ...

Artificial intelligence-driven discovery of YH395A: A novel TGFβR1 inhibitor with potent anti-tumor activity against triple-negative breast cancer.

Characterized by high malignancy and limited treatment efficacy, triple-negative breast cancer (TNBC) remains a clinically challenging subtype within ...

A computational study of cardiac glycosides from Vernonia amygdalina as PI3K inhibitors for targeting HER2 positive breast cancer.

The PI3K/Akt pathway plays a crucial role in regulating a broad network of proteins involved in the proliferation of HER2-positive breast cancer. The ...

Identification of inflammation-related biomarkers and therapeutic targets for neurogenic bladder fibrosis via multi-omics analysis.

Inflammatory responses play a crucial role in the progression of pediatric neurogenic bladder (NB)-associated fibrosis; however, their specific contri...

Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis.

This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning ...

Can ChatGPT Provide Patient-Friendly and Reliable Information on Cervical Cancer Screening? A Study of ChatGPT-Generated Information in Polish.

BACKGROUND Cervical cancer (CC) mortality remains a global health problem, and women's awareness of the need for regular CC screening is insufficient....

Deep molecular profiling of synovial biopsies in the STRAP trial identifies signatures predictive of treatment response to biologic therapies in rheumatoid arthritis.

Approximately 40% of patients with rheumatoid arthritis do not respond to individual biologic therapies, while biomarkers predictive of treatment resp...