Prescriptions

Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.

Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern recognition capabilities to extract and mine valuable drug resistance information from large amounts of clinical or omics data, to study drug resistan...

Artificial Intelligence: Applications in Pharmacovigilance Signal Management.

Pharmacovigilance is the science of collection, detection, and assessment of adverse events associated with pharmaceutical products for the ongoing monitoring and understanding of those products' safety profiles. Part of this process, signal management, encompasses the activities of signal detection, signal validation/confirmation, signal evaluation, and ultimately, final assessment as to whether ...

The prediction of RNA-small molecule binding sites in RNA structures based on geometric deep learning.

Biological interactions between RNA and small-molecule ligands play a crucial role in determining the specific functions of RNA, such as catalysis and...

A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.

Tuberculosis, a deadly and contagious disease caused by Mycobacterium tuberculosis, remains a significant global public health threat. HIV co-infectio...

A combined approach of evolutionary game and system dynamics for user privacy protection in human intelligence interaction.

The rapid development of generative artificial intelligence (GenAI) has generated significant economic and social value, alongside risks to user priva...

A random forest-based predictive model for classifying BRCA1 missense variants: a novel approach for evaluating the missense mutations effect.

The right classification of variants is the key to pre-symptomatic detection of disease and conducting preventive actions. Since BRCA1 has a high inci...

In-silico investigation integrated with machine learning to identify potential inhibitors targeting AKT2: Key driver of cancer cell progression and metastasis.

BACKGROUND AND OBJECTIVE: In search of a key driver for the invasive growth of cancer metastasis, AKT2 is found to be exceptionally expressed in color...

Effect of Uncertainty-Aware AI Models on Pharmacists' Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial.

BACKGROUND: Artificial intelligence (AI)-based clinical decision support systems are increasingly used in health care. Uncertainty-aware AI presents t...

DrugGen enhances drug discovery with large language models and reinforcement learning.

Traditional drug design faces significant challenges due to inherent chemical and biological complexities, often resulting in high failure rates in cl...

Artificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing.

Artificial intelligence (AI) is reshaping preclinical drug research offering innovative alternatives to traditional animal testing. Advanced technique...

Complementing but Not Replacing: Comparing the Impacts of GPT-4 and Native-Speaker Interaction on Chinese L2 Writing Outcomes.

This study explored the efficacy of large language models (LLMs), namely GPT-4, in supporting second language (L2) writing in comparison with interact...

ShenJiaoLingCao decoction ameliorates cyclophosphamide-induced splenic injury and immunosuppression via the inhibition of MEK/ERK signaling pathway activity and modulation of amino acid metabolism.

ETHNOPHARMACOLOGICAL RELEVANCE: ShenJiaoLingCao Decoction (SJLCD) is derived from the classic Chinese medicine prescription, which consists of ten kin...

An application of deep learning model InceptionTime to predict nausea, vomiting, diarrhoea, and constipation using the gastro-intestinal pacemaker activity drug database (GIPADD).

The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemake...

Machine learning approaches for assessing medication transfer to human breast milk.

The human milk/plasma (M/P) drug concentration ratio is crucial in pharmacology, especially for breastfeeding mothers undergoing treatment. It determi...

Changes in recreational drug use, reasons for those changes and their consequence during and after the COVID-19 pandemic in the UK.

Changes in drug use in the general population during the COVID-19 pandemic and their long-term consequences are not well understood. We employed natur...

Addressing Imbalanced Classification Problems in Drug Discovery and Development Using Random Forest, Support Vector Machine, AutoGluon-Tabular, and H2O AutoML.

The classification models built on class imbalanced data sets tend to prioritize the accuracy of the majority class, and thus, the minority class gene...

Towards improved prescription metrics in novel radiotherapy techniques: a machine learning study.

FLASH radiotherapy (RT), microbeam RT (MRT) and minibeam RT (MBRT) are novel RT techniques that have been shown to reduce normal tissue complication p...

Integrating advanced deep learning techniques for enhanced detection and classification of citrus leaf and fruit diseases.

In this study, we evaluate the performance of four deep learning models, EfficientNetB0, ResNet50, DenseNet121, and InceptionV3, for the classificatio...

A potential supplemental indication of dipyridamole for contraception: Dipyridamole inhibits mouse sperm fertilization capacity in vitro.

The inhibition of sperm cAMP and ATP levels, using an FDA-approved medication, may impair sperm motility and, consequently, fertilization, thus paving...

Enhancing the understandings on SARS-CoV-2 main protease (M) mutants from molecular dynamics and machine learning.

While star drugs like Paxlovid have shown remarkable performance in combating SARS-CoV-2, we still face serious challenges such as viral mutants and r...