AIMC Topic: Humans

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Sparse autoencoders uncover biologically interpretable features in protein language model representations.

Proceedings of the National Academy of Sciences of the United States of America
Foundation models in biology-particularly protein language models (PLMs)-have enabled ground-breaking predictions in protein structure, function, and beyond. However, the "black-box" nature of these representations limits transparency and explainabil...

Efficient neural encoding as revealed by bilingualism.

Proceedings of the National Academy of Sciences of the United States of America
The remarkable human capacity for bilingual and multilingual acquisition raises fundamental questions about how the brain develops efficient systems for processing multiple languages. In this study, we used neural network models trained on natural sp...

Improving risk stratification of PI-RADS 3 + 1 lesions of the peripheral zone: expert lexicon of terms, multi-reader performance and contribution of artificial intelligence.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: According to PI-RADS v2.1, peripheral PI-RADS 3 lesions are upgraded to PI-RADS 4 if dynamic contrast-enhanced MRI is positive (3+1 lesions), however those lesions are radiologically challenging. We aimed to define criteria by expert cons...

Metabolomics and nutrient intake reveal metabolite-nutrient interactions in metabolic syndrome: insights from the Korean Genome and Epidemiology Study.

Nutrition journal
BACKGROUND: Despite advances in metabolomics, the complex relationship between metabolites and nutrient intake in metabolic syndrome (MetS) remains poorly understood in the Korean population.

A machine learning approach to predict self-efficacy in breast cancer survivors.

BMC medical informatics and decision making
PURPOSE: To determine predictors of self-efficacy in breast cancer survivors and identify vulnerable groups.

Factors that influence technophobia in Chinese older patients with ischemic stroke: a cross-sectional survey.

BMC geriatrics
BACKGROUND: Older patients with ischemic stroke often have a large number of medical needs, technophobia refers to the irrational anxiety and fear of digital technologies such as mobile communication equipment, artificial intelligence and robots, res...

Effectiveness of spiritual health-based interventions in improving health indicators of patients in Iran: a systematic review and meta-analysis.

BMC psychology
Spiritual health interventions have increasingly been recognized for their potential to improve general health outcomes. This study undertakes a systematic review and meta-analysis to evaluate their effectiveness on patient health in Iran. Data were ...

Application of deep learning reconstruction at prone position chest scanning of early interstitial lung disease.

BMC medical imaging
AIM: Timely intervention of interstitial lung disease (ILD) was promising for attenuating the lung function decline and improving clinical outcomes. The prone position HRCT is essential for early diagnosis of ILD, but limited by its high radiation ex...

Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVE: This study aimed to construct a multimodal imaging deep learning (DL) model integrating mpMRI and F-PSMA-PET/CT for the prediction of extraprostatic extension (EPE) in prostate cancer, and to assess its effectiveness in enhancing the diagn...

Predictive value of anthropometric indices for incident of dyslipidemia: a large population-based study.

Population health metrics
INTRODUCTION: Dyslipidemia as a modifiable risk factor for chronic non-communicable diseases has become a worldwide concern. We aim to explore different anthropometric measures as predictors of dyslipidemia using various machine learning methods.