AIMC Topic: Administration, Oral

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Oral ENPP1 inhibitor designed using generative AI as next generation STING modulator for solid tumors.

Nature communications May 23, 2025
Despite the STING-type-I interferon pathway playing a key role in effective anti-tumor immunity, the therapeutic benefit of direct STING agonists appears limited. In this study, we use several artificial intelligence techniques and patient-based mult...
Pyrophosphatases Nucleotides, Cyclic Membrane Proteins Mice, Inbred C57BL Administration, Oral Phosphoric Diester Hydrolases Humans Artificial Intelligence Female Drug Design Cell Line, Tumor Mice Animals Neoplasms
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Can ChatGPT provide parent education for oral immunotherapy?

Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology Apr 24, 2025
BACKGROUND: Oral Immunotherapy (OIT) has exhibited great potential in the treatment of food allergy. However, there is no global consensus on best practices of OIT. Parents of allergic children often struggle with concerns regarding OIT methodology, ...
Humans Artificial Intelligence Female Parents Patient Education as Topic Food Hypersensitivity Administration, Oral Desensitization, Immunologic Male Surveys and Questionnaires Child Generative Artificial Intelligence
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Safety assessment of the ethanolic extract of Siparuna guianensis: Cell viability, molecular risk predictions and toxicity risk for acute and sub-chronic oral ingestion.

Journal of ethnopharmacology Apr 5, 2025
ETHNOPHARMACOLOGICAL RELEVANCE: The species Siparuna guianensis Aublet (family Siparunaceae) is traditionally used by indigenous peoples and riverine communities in Central and South America to treat migraines, flu, respiratory diseases, fever, pain,...
Ethanol Dose-Response Relationship, Drug Cell Survival Plant Leaves Lethal Dose 50 Toxicity Tests, Acute Toxicity Tests, Subchronic Administration, Oral Mice Female Plant Extracts Solvents Animals Rats, Wistar Male Rats Risk Assessment
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Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant.

The American journal of cardiology Feb 25, 2025
Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized care. Alternatives like left atrial appendage closure devices lower stroke risk with fewer nonprocedural blee...
Machine Learning Retrospective Studies Female Risk Factors Hospitalization Humans Hemorrhage Anticoagulants Stroke Aged Administration, Oral Male Atrial Fibrillation Middle Aged Risk Assessment
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Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.

Scientific reports Jan 4, 2025
We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally ac...
Humans Machine Learning Algorithms Kidney Kidney Diseases Pharmaceutical Preparations Administration, Oral Quantitative Structure-Activity Relationship
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Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.

European journal of clinical investigation Dec 11, 2024
BACKGROUND: Although oral anticoagulation decreases the risk of thromboembolism in patients with atrial fibrillation (AF), a residual risk of thrombotic events still exists. This study aimed to construct machine learning (ML) models to predict the re...
Administration, Oral Atrial Fibrillation Ischemic Attack, Transient Thromboembolism Registries Stroke Myocardial Infarction Thrombosis Logistic Models Ischemic Stroke Area Under Curve Anticoagulants Algorithms Humans Middle Aged Female Machine Learning Risk Assessment Aged, 80 and over Risk Factors ROC Curve Aged Male
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Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications.

Methods (San Diego, Calif.) Nov 1, 2024
Diabetes management is often complicated by comorbidities, requiring complex medication regimens that increase the risk of drug-drug interactions (DDIs), potentially compromising treatment outcomes or causing toxicity. Although machine learning (ML) ...
Diabetes Mellitus Drug Interactions Administration, Oral Humans Machine Learning Hypoglycemic Agents
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High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models.

Journal of hazardous materials Oct 28, 2024
This study utilized available oral acute toxicity data in Rat and Mouse for polychlorinated persistent organic pollutants (PC-POPs) to construct data fusion-driven machine learning (ML) global models. Based on atom-centered fragments (ACFs), the coll...
High-Throughput Screening Assays Hydrocarbons, Chlorinated Toxicity Tests, Acute Persistent Organic Pollutants Mice Rats Administration, Oral Machine Learning Animals
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Bowel preparation before elective right colectomy: Multitreatment machine-learning analysis on 2,617 patients.

Surgery Sep 25, 2024
BACKGROUND: In the worldwide, real-life setting, some candidates for right colectomy still receive no bowel preparation, some receive oral antibiotics alone, some receive mechanical bowel preparation alone, and some receive mechanical bowel preparati...
Middle Aged Elective Surgical Procedures Administration, Oral Surgical Wound Infection Propensity Score Cathartics Preoperative Care Colectomy Anastomotic Leak Female Aged Adult Male Antibiotic Prophylaxis Humans Machine Learning Anti-Bacterial Agents
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Application of machine-learning models to predict the ganciclovir and valganciclovir exposure in children using a limited sampling strategy.

Antimicrobial agents and chemotherapy Aug 28, 2024
Intravenous ganciclovir and oral valganciclovir display significant variability in ganciclovir pharmacokinetics, particularly in children. Therapeutic drug monitoring currently relies on the area under the concentration-time (AUC). Machine-learning (...
Ganciclovir Male Infant Adolescent Child, Preschool Humans Machine Learning Drug Monitoring Child Algorithms Cytomegalovirus Infections Administration, Oral Antiviral Agents Female Administration, Intravenous Area Under Curve Valganciclovir Bayes Theorem
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