Prescriptions

Antimicrobial Activity of Tea and Agarwood Leaf Extracts Against Multidrug-Resistant Microbes.

Emerging multidrug-resistant (MDR) strains are the main challenges to the progression of new drug discovery. To diminish infectious disease-causing pathogens, new antibiotics are required while the drying pipeline of potent antibiotics is adding to the severity. Plant secondary metabolites or phytochemicals including alkaloids, phenols, flavonoids, and terpenes have successfully demonstrated their...

Core reference ontology for individualized exercise prescription.

"Exercise is medicine" emphasizes personalized prescriptions for better efficacy. Current guidelines need more support for personalized prescriptions, posing scientific challenges. Facing those challenges, we gathered data from established guidelines, databases, and articles to develop the Exercise Medicine Ontology (EXMO), intending to offer comprehensive support for personalized exercise prescri...

Novel artificial intelligence-based identification of drug-gene-disease interaction using protein-protein interaction.

The evaluation of drug-gene-disease interactions is key for the identification of drugs effective against disease. However, at present, drugs that are...

AI-Driven Drug Discovery for Rare Diseases.

Rare diseases (RDs), affecting 300 million people globally, present a daunting public health challenge characterized by complexity, limited treatment ...

Improving citizen-government interactions with generative artificial intelligence: Novel human-computer interaction strategies for policy understanding through large language models.

Effective communication of government policies to citizens is crucial for transparency and engagement, yet challenges such as accessibility, complexit...

Investigating Older Adults' Perceptions of AI Tools for Medication Decisions: Vignette-Based Experimental Survey.

BACKGROUND: Given the public release of large language models, research is needed to explore whether older adults would be receptive to personalized m...

Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine.

Artificial Intelligence (AI) and Machine Learning (ML) are transforming drug discovery by overcoming traditional challenges like high costs, time-cons...

How accurate are machine learning models in predicting anti-seizure medication responses: A systematic review.

IMPORTANCE: Current epilepsy management protocols often depend on anti-seizure medication (ASM) trials and assessment of clinical response. This may d...

Fostering children's creativity through LLM-driven storytelling with a social robot.

Creativity is an important skill that is known to plummet in children when they start school education that limits their freedom of expression and the...

The native Iranian soil bacteria with high potential to produce extracellular methionine gamma-lyase.

This study aimed to screen native methionine gamma-lyase (L-methioninase) producing bacteria from soil samples and optimize the culture media for enha...

Deep Learning for Detecting and Subtyping Renal Cell Carcinoma on Contrast-Enhanced CT Scans Using 2D Neural Network with Feature Consistency Techniques.

 The aim of this study was to explore an innovative approach for developing deep learning (DL) algorithm for renal cell carcinoma (RCC) detection and...

Improving binding affinity prediction by emphasizing local features of drug and protein.

Binding affinity prediction has been considered as a fundamental task in drug discovery. Despite much effort to improve accuracy of binding affinity p...

[Current status and outlooks of acupuncture research driven by machine learning].

The machine learning is used increasingly and widely in acupuncture prescription optimization, intelligent treatment and precision medicine, and has o...

SAGCN: Using Graph Convolutional Network With Subgraph-Aware for circRNA-Drug Sensitivity Identification.

Circular RNAs (circRNAs) play a significant role in cancer development and therapy resistance. There is substantial evidence indicating that the expre...

MMD-DTA: A Multi-Modal Deep Learning Framework for Drug-Target Binding Affinity and Binding Region Prediction.

The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identification of potential drug targets. In recent year...

A Protein-Context Enhanced Master Slave Framework for Zero-Shot Drug Target Interaction Prediction.

Drug Target Interaction (DTI) prediction plays a crucial role in in-silico drug discovery, especially for deep learning (DL) models. Along this line, ...

A Knowledge Graph-Based Method for Drug-Drug Interaction Prediction With Contrastive Learning.

Precisely predicting Drug-Drug Interactions (DDIs) carries the potential to elevate the quality and safety of drug therapies, protecting the well-bein...

Combined effect of Tetracycline compounds and essential oils on antimicrobial resistant isolated from the swine food chain.

Antimicrobial resistance (AMR) poses risks for food stakeholders because of the spread of resistant microbes and potential foodborne diseases. In exam...

Heparin in sepsis: current clinical findings and possible mechanisms.

Sepsis is a clinical syndrome resulting from the interaction between coagulation, inflammation, immunity and other systems. Coagulation activation is ...

Securing healthcare medical image information using advance morphological component analysis, information hiding systems, and hybrid convolutional neural networks on IoMT.

Health care images contain a variety of imaging information that has specific features, which can make it challenging to assess and decide on the meth...