Prescriptions

Contemporary digital marketing techniques used in unhealthy food campaigns targeting young people.

The digital marketing of unhealthy foods and non-alcoholic beverages has a detrimental impact on children's eating behaviours, leading to adverse diet-related health outcomes. To inform the development of evidence-based strategies to protect children online, this study aimed to describe contemporary digital marketing techniques and industry reported impacts from unhealthy food and beverage marketi...

Niuhuang jiedu prescription alleviates realgar-induced dopaminergic and GABAergic neurotoxicity in Caenorhabditis elegans.

ETHNOPHARMACOLOGICAL RELEVANCE: Niuhuang Jiedu (NHJD) is a Chinese medicine prescription containing realgar (AsS), which is neurotoxic, and seven other traditional Chinese medicines (TCMs). However, whether the multiple TCMs contained in NHJD can mitigate the neurotoxicity of realgar is yet to be elucidated.

Updates in Neonatal Seizures.

Neonatal seizures are a common medical emergency, necessitating prompt treatment. The most common etiologies include hypoxic-ischemic encephalopathy, ...

Machine Learning-Enhanced Prediction for Soil-to-Air VOC Emission and Environmental Impact Pertaining Contaminated Fractured Aquifers.

How to scientifically and efficiently quantify the impact and hazards of volatile organic compounds (VOCs) pollution and volatilization from complex g...

A robust and omnidirectional-sensitive electronic antenna for tactile-induced perception.

Skin-like planar tactile sensors have achieved adaptive gripping, in-hand manipulation, and human-machine interaction but remain limited in tasks requ...

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer ...

Predicting protein-protein interaction with interpretable bilinear attention network.

BACKGROUND AND OBJECTIVE: Protein-protein interactions (PPIs) play the key roles in myriad biological processes, helping to understand the protein fun...

pACPs-DNN: Predicting anticancer peptides using novel peptide transformation into evolutionary and structure matrix-based images with self-attention deep learning model.

Globally, cancer remains a major health challenge due to its high mortality rates. Traditional experimental approaches and therapies are resource-inte...

Unveiling drug-induced osteotoxicity: A machine learning approach and webserver.

Drug-induced osteotoxicity refers to the harmful effects certain pharmaceuticals have on the skeletal system, posing significant safety risks. These t...

Binding interaction of acetylcholinesterase with steroidal pregnanes: insight from machine learning and atomistic simulation.

Acetylcholinesterase (AChE) inhibition is a key strategy in the treatment of Alzheimer's disease and other neurodegenerative disorders. While pregnane...

Stability of Jurkat cells during short-term liquid storage analyzed by flow imaging microscopy.

The viability of cell-based medicinal products (CBMPs) is a critical quality attribute and must be assessed throughout the product lifecycle to contri...

SGCLMD: Signed graph-based contrastive learning model for predicting somatic mutation-drug association.

Somatic mutations could influence critical cellular processes, leading to uncontrolled cell growth and tumor formation. Understanding the intricate in...

Knowledge-Based Artificial Intelligence System for Drug Prioritization.

drug prioritization may be a promising and time-saving strategy to identify potential drugs, standing as a faster and more cost-effective approach th...

Insights into phosphorylation-induced influences on conformations and inhibitor binding of CDK6 through GaMD trajectory-based deep learning.

The phosphorylation of residue T177 produces a significant effect on the conformational dynamics of CDK6. Gaussian accelerated molecular dynamics (GaM...

SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction.

Drug-target interaction (DTI) prediction is pivotal in drug discovery and repurposing, providing a more efficient alternative to traditional wet-lab e...

NNSFMDA: Lightweight Transformer Model with Bounded Nuclear Norm Minimization for Microbe-Drug Association Prediction.

Identifying potential connections between microbe-drug pairs play an important role in drug discovery and clinical treatment. Techniques like graph ne...

Towards AI-Powered Applications: The Development of a Personalised LLM for HRI and HCI.

In this work, we propose a novel Personalised Large Language Model (PLLM) agent, designed to advance the integration and adaptation of large language ...

Development of an artificial intelligence-enhanced warfarin interaction checker platform.

Warfarin is a common anticoagulant drug for thrombo-prophylaxis in stroke and venous thromboembolism, which has many advantages but also some disadvan...

Leveraging artificial intelligence to assess the impact of COVID-19 on the teacher-student relationship in higher education.

The teacher-student relationship has far-reaching implications for educational outcomes at the tertiary level. Teachers contribute to students' succes...

A longitudinal observational study with ecological momentary assessment and deep learning to predict non-prescribed opioid use, treatment retention, and medication nonadherence among persons receiving medication treatment for opioid use disorder.

BACKGROUND: Despite effective treatments for opioid use disorder (OUD), relapse and treatment drop-out diminish their efficacy, increasing the risks o...