AIMC Topic: Metabolic Diseases
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Nuciferine activates intestinal TAS2R46 to attenuate metabolic disorders and hyperlipidemia via hepatic VLDL regulation.
Phytomedicine : international journal of phytotherapy and phytopharmacology
Apr 22, 2025
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
Mar 28, 2025
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
Mar 19, 2025
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
Mar 1, 2025
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
Feb 18, 2025
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
Dec 22, 2024
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...
Identification of immune-related mitochondrial metabolic disorder genes in septic shock using bioinformatics and machine learning.
Hereditas
Nov 28, 2024
PURPOSE: Mitochondria are involved in septic shock and inflammatory response syndrome, which severely affects the life security of patients. It is necessary to recognize and explore the immune-mitochondrial genes in septic shock.
Mesocorticolimbic and Cardiometabolic Diseases-Two Faces of the Same Coin?
International journal of molecular sciences
Sep 6, 2024
The risk behaviors underlying the most prevalent chronic noncommunicable diseases (NCDs) encompass alcohol misuse, unhealthy diets, smoking and sedentary lifestyle behaviors. These are all linked to the altered function of the mesocorticolimbic (MCL)...
Digital twins and artificial intelligence in metabolic disease research.
Trends in endocrinology and metabolism: TEM
May 13, 2024
Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-...
Engineering novel scaffolds for specific HDAC11 inhibitors against metabolic diseases exploiting deep learning, virtual screening, and molecular dynamics simulations.
International journal of biological macromolecules
Feb 8, 2024
The prevalence of metabolic diseases is increasing at a frightening rate year by year. The burgeoning development of deep learning enables drug design to be more efficient, selective, and structurally novel. The critical relevance of Histone deacetyl...
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