AIMC Topic: Lipids

Clear Filters Showing 21 to 30 of 168 articles

A Machine Learning Model for the Proteome-Wide Prediction of Lipid-Interacting Proteins.

Journal of chemical information and modeling
Lipids are essential metabolites that play critical roles in multiple cellular pathways. Like many primary metabolites, mutations that disrupt lipid synthesis can be lethal. Proteins involved in lipid synthesis, trafficking, and modification, are tar...

CLAW-MRM: omprehensive ipidomics utomation orkflow for ultiple eaction onitoring Using Large Language Models.

Analytical chemistry
Lipidomic profiling generates vast datasets, making manual annotation and trend interpretation complex and time-intensive. The structural and chemical diversity of the lipidome further complicates the analysis. While existing tools support targeted l...

Adipocyte-selective mRNA lipid nanoparticles for cell programming with machine learning analysis.

Journal of controlled release : official journal of the Controlled Release Society
Adipose tissue plays a crucial role in energy metabolism and endocrine signaling. White adipose tissue (WAT), in particular, is a compelling target for therapeutic interventions in metabolic diseases due to its secretory capacity and abundance. Gene ...

Computationally unmasking each fatty acyl C=C position in complex lipids by routine LC-MS/MS lipidomics.

Nature communications
Identifying carbon-carbon double bond (C=C) positions in complex lipids is essential for elucidating physiological and pathological processes. Currently, this is impossible in high-throughput analyses of native lipids without specialized instrumentat...

Advances in mass spectrometry of lipids for the investigation of Niemann-pick type C disease.

Lipids in health and disease
Niemann-Pick type C (NPC) disease is a devastating, fatal, neurodegenerative disease and a form of lysosomal storage disorder. It is caused by mutations in either NPC1 or NPC2 genes, leading to the accumulation of cholesterol and other lipids in the ...

Exploration of Predictive Potential of AI-enabled Portable System in Anticancer Drug Delivery: A Comparative Study with Modified Gompertz like Biphasic Response Model.

AAPS PharmSciTech
Mathematical models are conventionally used to understand the of tumor behaviors, but they generally lack in precisely correlating drug efficacy with tumor response. Artificial intelligence (AI) has forged a new avenue in cancer management, but requi...

Urine-based Raman markers for prostate cancer diagnosis: A machine learning approach using fingerprint and lipid spectral region.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study investigates the potential of Raman spectroscopy in distinguishing between healthy individuals and prostate cancer patients using urine samples. The Boruta algorithm was applied to Raman spectral data in two distinct wavenumber regions: 80...

Linking lipidomics to meat quality: A review on texture and flavor in livestock and poultry.

Food chemistry
This review explores the application of lipidomics in livestock and poultry meat research, focusing on lipid identification and analysis from the perspectives of meat quality and flavor. We offer a comprehensive overview of lipid extraction methods a...

Lipidomic profiling of human adiposomes identifies specific lipid shifts linked to obesity and cardiometabolic risk.

JCI insight
BACKGROUNDObesity, a growing health concern, often leads to metabolic disturbances, systemic inflammation, and vascular dysfunction. Emerging evidence suggests that adipose tissue-derived extracellular vesicles (adiposomes) may propagate obesity-rela...