Hemophilia

Ameliorating Racial Disparities in HIV Prevention via a Nurse-Led, AI-Enhanced Program for Pre-Exposure Prophylaxis Utilization Among Black Cisgender Women: Protocol for a Mixed Methods Study.

BACKGROUND: HIV pre-exposure prophylaxis (PrEP) is a critical biomedical strategy to prevent HIV transmission among cisgender women. Despite its proven effectiveness, Black cisgender women remain significantly underrepresented throughout the PrEP care continuum, facing barriers such as limited access to care, medical mistrust, and intersectional racial or HIV stigma. Addressing these disparities i...

A two-stage ensemble learning based prediction and grading model for PD-1/PD-L1 inhibitor-related cardiac adverse events: A multicenter retrospective study.

BACKGROUND: Immune-related cardiac adverse events (ircAEs) caused by programmed cell death protein-1 (PD-1) and programmed death-ligand-1 (PD-L1) inhibitors can lead to fulminant and even fatal consequences. This study aims to develop a prediction and grading model for ircAEs, enabling graded management of patients.

Cheminformatics analysis of indoleamine and tryptophan 2,3-dioxygenase inhibitors: A descriptor and fingerprint based machine learning approach to disclose selectivity measures.

Indoleamine 2,3-dioxygenase (IDO) and tryptophan 2,3-dioxygenase (TDO) are attractive drug targets for cancer immunotherapy. After disappointing resul...

In silico method and bioactivity evaluation to discover novel antimicrobial agents targeting FtsZ protein: Machine learning, virtual screening and antibacterial mechanism study.

This research paper utilizes a fused-in-silico approach alongside bioactivity evaluation to identify active FtsZ inhibitors for drug discovery. Initia...

Prognostic significance of migrasomes in neuroblastoma through machine learning and multi-omics.

This study explores migrasomes' role in neuroblastoma, a common malignant tumor in children, and their potential impact on tumor formation. We analyze...

Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning.

Inhibiting MDM2-p53 interaction is considered an efficient mode of cancer treatment. In our current study, Gaussian-accelerated molecular dynamics (Ga...

Redefining a new frontier in alkaptonuria therapy with AI-driven drug candidate design via innovation.

A rare metabolic condition called alkaptonuria (AKU) is caused by a decrease in homogentisate 1,2 dioxygenase (HGO) activity due to a mutation in homo...

A novel CAF-cancer cell crosstalk-related gene prognostic index based on machine learning: prognostic significance and prediction of therapeutic response in head and neck squamous cell carcinoma.

BACKGROUND: Cancer-associated fibroblast (CAF)-cancer cell crosstalk (CCCT) plays an important role in tumor microenvironment shaping and immunotherap...

Construction of IRAK4 inhibitor activity prediction model based on machine learning.

Interleukin-1 receptor-associated kinase 4 (IRAK4) is a crucial serine/threonine protein kinase that belongs to the IRAK family and plays a pivotal ro...

Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides-Triazole Hybrids with Anticancer Activity.

In the presented work, a series of 22 hybrids of 8-quinolinesulfonamide and 1,4-disubstituted triazole with antiproliferative activity were designed a...

Discovery of a Novel and Potent LCK Inhibitor for Leukemia Treatment via Deep Learning and Molecular Docking.

The lymphocyte-specific protein tyrosine kinase (LCK) plays a crucial role in both T-cell development and activation. Dysregulation of LCK signaling h...

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emergi...

Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design.

Determining the viability of a new drug molecule is a time- and resource-intensive task that makes computer-aided assessments a vital approach to rapi...

Application of molecular dynamics-based pharmacophore and machine learning approaches to identify novel Mcl1 inhibitors through drug repurposing and mechanics research.

Myeloid cell leukemia 1 (Mcl1), a critical protein that regulates apoptosis, has been considered as a promising target for antitumor drugs. The conven...

Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning.

CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transiti...

A Scalable Application of Artificial Intelligence-Driven Insulin Titration Program to Transform Type 2 Diabetes Management.

Despite new pharmacotherapy, most patients with long-term type 2 diabetes are still hyperglycemic. This could have been solved by insulin with its un...

Achieving large-scale clinician adoption of AI-enabled decision support.

Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making...

Machine Learning-Enhanced Quantum Chemistry-Assisted Refinement of the Active Site Structure of Metalloproteins.

Understanding the fine structural details of inhibitor binding at the active site of metalloenzymes can have a profound impact on the rational drug de...

Prediction of inhibitor development in previously untreated and minimally treated children with severe and moderately severe hemophilia A using a machine-learning network.

BACKGROUND: Prediction of inhibitor development in patients with hemophilia A (HA) remains a challenge.

The potential use of artificial intelligence for venous thromboembolism prophylaxis and management: clinician and healthcare informatician perspectives.

Venous thromboembolism (VTE) is the leading cause of preventable death in hospitalized patients. Artificial intelligence (AI) and machine learning (ML...