Hemophilia

Integrating machine learning-based molecular design with experimental validation for the discovery of EGFR inhibitors in lung cancer.

The emergence of drug resistance and off-target toxicities in epidermal growth factor receptor (EGFR) targeted therapies underscores the urgent need for novel inhibitor scaffolds. This study integrates artificial intelligence-driven generative models with experimental validation to discover novel, selective EGFR inhibitors. Utilizing REINVENT4, a reinforcement learning-based generative framework, ...

Target-driven machine learning-enabled virtual screening (TAME-VS) enables prioritization of AKR1C3 inhibitors.

Machine learning-guided virtual screening has the potential to accelerate early-stage drug discovery, but its practical utility is often limited by uncertain generalization to different chemical scaffolds and by insufficient prospective evaluation under realistic experimental constraints. To address these challenges, we conduct a target-driven virtual screening study using TAME-VS against aldo-ket...

Fusion prediction model for post-ERCP pancreatitis under NSAIDs prophylaxis.

BACKGROUND: Endoscopic retrograde cholangiopancreatography (ERCP) is a common procedure for treating biliary and pancreatic disorders; however, it is ...

Long-term outcomes with emicizumab prophylaxis for haemophilia A in China: A multicentre, large-cohort retrospective study.

Emicizumab is an established prophylactic therapy for haemophilia A (HA). However, real-world multicentre evidence on long-term outcomes, particularly...

Atomic Insights into Corrosion of Cobalt in Aqueous Environment: Development of ReaxFF with an Active Learning Framework.

Cobalt (Co) has emerged as a promising interconnect material to replace copper. Due to its susceptibility to corrosion, controlling the balance betwee...

Machine learning-assisted screening of natural product database for the identification of novel chalcone-based derivative as a potent DHODH inhibitor in cancer therapy.

BACKGROUND: Dihydroorotate dehydrogenase (DHODH), an important enzyme in de-novo pyrimidine synthesis, and its dysregulation has been frequently assoc...

RRBP1 promotes osimertinib resistance by activating fatty acid oxidation-dependent metabolic and immune reprogramming in EGFR-Mutant lung adenocarcinoma.

Osimertinib is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that improves clinical outcomes in patients with E...

Recent advances in biomass deconstruction, microbial conversion, artificial intelligence, and carbon capture for sustainable bioenergy.

The transition from fossil fuels to low-carbon bio-based energy systems is increasingly constrained by the efficiency, scalability, and integration of...

Exploring celecoxib polymorph landscape using AIMNet2 machine learning interatomic potential.

The crystal form a drug adopts can change everything from how it dissolves to whether it works in the clinic, yet predicting which polymorphs a flexib...

Pre-implementation safety evaluation of an AI decision-support system for surgical antimicrobial prophylaxis.

BACKGROUND: Large language models (LLMs) show potential to support antimicrobial prescribing but require simulation-based, institution-specific safety...

Development and External Validation of a Transcriptome-Based Multivariable Prediction Model for Treatment-Free Remission in Chronic Myeloid Leukemia.

PURPOSE: Treatment-free remission (TFR) is a major therapeutic objective in chronic myeloid leukemia (CML). However, nearly 50% of patients relapse af...

Ketone Body Metabolism and Sodium Glucose Cotransporter 2 Inhibitor Response in Heart Failure With Ischemic Cause: A Precision Medicine Approach Using Metabolomics Integration With Global Burden of Disease Population Data Across Diverse Genetic Backgrounds.

BACKGROUND: Heart failure with ischemic cause is associated with substantial cardiovascular mortality. SGLT2 (sodium glucose cotransporter 2) inhibito...

Updating CMV protocols in lung transplant patients: a single-center case study modeling use of generative AI for antimicrobial stewardship protocol development and economic impact analysis.

OBJECTIVE: Antimicrobial Stewardship Programs (ASPs) need healthcare economic analyses to support and inform ASP strategies. This work aimed to determ...

First-trimester multi-modal cell-free DNA analysis for prediction of preterm and term preeclampsia.

BACKGROUND: Preeclampsia, which is a leading cause of maternal and perinatal mortality and morbidity, represents a biologically heterogeneous syndrome...

Comprehensive multi-omics analysis identifies cancer subtypes and prognostic signatures of hepatitis B virus-associated hepatocellular carcinoma.

Hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) is a heterogeneous malignancy with poor prognosis, necessitating refined classificatio...

Epigenetic reprogramming of tissue-resident memory T cells in chronic inflammatory disorders and implications for targeted therapies.

BACKGROUND: Tissue-resident memory T (TRM) cells play a role in causing long-term tissue injury in chronic inflammatory diseases via pathological epig...

A multimodal deep learning model predicting hyperprogressive disease for PD-1 blockade in advanced hepatocellular carcinoma.

Immune checkpoint inhibitors, particularly antibodies targeting programmed cell death 1 (PD-1), are increasingly used for advanced hepatocellular carc...

An integrated machine learning and mendelian randomization approach identifies SERPING1 as a prognostic biomarker associated with CD8 + T-cell infiltration in DLBCL.

BACKGROUND: SERPING1, which encodes the C1 inhibitor (C1-INH) of the complement system, and plays a key regulator in regulating inflammatory responses...

Deep learning insights into β-lactamase dynamics and resistance evolution.

The rapid global expansion of β-lactamase-mediated antimicrobial resistance demands mechanistic approaches capable of resolving the dynamics of enzyme...

Association between completing at least eight antenatal care contacts and maternal anaemia in Ghana: a cross-sectional study using causal machine learning.

BACKGROUND: Maternal anaemia remains a pressing global health challenge, with a notable burden in low- and middle-income countries. Existing studies i...