AIMC Topic: Metabolic Diseases

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The Central Role of m6A as Epigenetic Regulator in Metabolic Disorders of Therapeutic Potential and Clinical Implications.

Molecular neurobiology
N6-methyladenosine (m6A) is the most common reversible mRNA modification, regulating fundamental cellular processes. It plays a vital role in aging and age-related diseases by influencing gene expression, RNA splicing, and stability. Growing evidence...

Photoresponsive HOF-Based Platforms for Large Language Model-Assisted Multimodal Diagnosis of Metabolic Diseases.

Analytical chemistry
Artificial intelligence (AI), particularly large language models (LLMs) such as chat generative pretrained transformer (ChatGPT), is revolutionizing various fields. Here, we present an AI-enhanced olfactory diagnosis platform that integrates a photor...

Nuclear receptors in metabolic, inflammatory, and oncologic diseases: mechanisms, therapeutic advances, and future directions.

European journal of medical research
Nuclear receptors (NRs) are a superfamily of ligand-activated transcription factors that regulate gene expression in response to metabolic, hormonal, and environmental signals. These receptors play a critical role in metabolic homeostasis, inflammati...

Nuciferine activates intestinal TAS2R46 to attenuate metabolic disorders and hyperlipidemia via hepatic VLDL regulation.

Phytomedicine : international journal of phytotherapy and phytopharmacology
BACKGROUND: Dysregulated blood lipid metabolism, a primary driver of hyperlipidemia, is closely associated with excessive very low-density lipoprotein (VLDL) synthesis and secretion. Nuciferine, a bioactive compound isolated from lotus leaves, demons...

Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics.

Journal of translational medicine
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health concern that necessitates early screening and timely intervention to improve prognosis. The current diagnostic protocols for MASLD involve complex procedu...

Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Nature communications
Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and v...

Spatial analysis of air pollutant exposure and its association with metabolic diseases using machine learning.

BMC public health
BACKGROUND: Metabolic diseases (MDs), exemplified by diabetes, hypertension, and dyslipidemia, have become increasingly prevalent with rising living standards, posing significant public health challenges. The MDs are influenced by a complex interplay...

Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a "seek out" machine learning approach.

Translational psychiatry
Binge eating disorder (BED) carries a 6 times higher risk for obesity and accounts for roughly 30% of type 2 diabetes cases. Timely identification of early glycemic disturbances and comprehensive treatment can impact on the likelihood of associated m...

Machine learning analysis of CD4+ T cell gene expression in diverse diseases: insights from cancer, metabolic, respiratory, and digestive disorders.

Cancer genetics
CD4 T cells play a pivotal role in the immune system, particularly in adaptive immunity, by orchestrating and enhancing immune responses. CD4 T cell-related immune responses exhibit diverse characteristics in different diseases. This study utilizes g...