AIMC Topic: Humans

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Dual-Channel Multiscale Graph Transformer with Adversarial Contrastive Learning and Low-Rank Disentangled Stratified Negative Sampling for Drug Repositioning.

Journal of chemical information and modeling
Drug repositioning accelerates therapeutic discovery, but existing computational methods are hampered by representation collapse, noisy supervision, and suboptimal negative sampling. To address these limitations, we introduce MGTAL-DR, a novel graph ...

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design.

Journal of chemical information and modeling
Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural ...

Asymmetric Dynamics Between the Protomers of the σ2 Receptor Homodimer.

Journal of chemical information and modeling
The sigma-2 receptor (σR/TMEM97) is a clinically relevant membrane protein involved in cholesterol regulation and overexpressed in cancer and neurodegenerative diseases. Despite its therapeutic potential, the dynamic mechanisms underlying σR function...

Prognostic Value of a Coronary Computed Tomography Angiography-Derived Ischemia Algorithm: Comparison Against Hybrid Coronary Computed Tomography Angiography/Positron Emission Tomography Imaging.

Journal of the American Heart Association
BACKGROUND: Artificial intelligence-guided quantitative computed tomography ischemia (AI-QCT) is a novel machine-learning method for predicting myocardial ischemia from coronary computed tomography angiography (CCTA). This observational cohort study ...

Multi-omics-based decoding of circulating biomarkers in amyotrophic lateral sclerosis and risks in environmental toxins.

BMC pharmacology & toxicology
BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the interplay of genetic and environmental factors, and currently, there there is a lack of effective diagnostic or therapeutic strategies available...

From dry eye to depression: a machine learning-based framework for predicting adolescent mental health.

BMC medical informatics and decision making
BACKGROUND: Adolescent depression is a major public health concern. Physical health indicators are rarely included in risk tools. We examined whether adding dry eye disease (DED) to psychosocial and behavioral factors improves prediction of depressiv...

Treatment decision support for esophageal cancer based on PET/CT data using deep learning.

BMC medical informatics and decision making
BACKGROUND: Making precise treatment decisions in esophageal cancer is essential for enhancing patient outcomes and avoiding overtreatment. Traditional approaches relying on special features or shallow learning models often fail to capture the comple...

TransST: transfer learning embedded spatial factor modeling of spatial transcriptomics data.

BMC bioinformatics
BACKGROUND: Spatial transcriptomics have emerged as a powerful tool in biomedical research because of its ability to capture both the spatial contexts and abundance of the complete RNA transcript profile in organs of interest. However, limitations of...

Progress and Prospects of Transdermal Treatment of Allergic Skin Diseases with Natural Drugs based on Nanotechnology.

AAPS PharmSciTech
Allergic skin disease conditions represent a significant global health challenge, with conventional therapies frequently associated with local dermal irritation and systemic adverse effects. Nanotechnology-enabled transdermal drug delivery platforms ...

Bi-directional ConvLSTM networks for early recognition of human activities and action prediction.

Scientific reports
Early detection of human activity is essential in domains including robotics, entertainment, surveillance, and healthcare. Early detection that is accurate enables prompt decision-making, enhancing system responsiveness and overall effectiveness. Con...