AIMC Topic: Tandem Mass Spectrometry

Clear Filters Showing 1 to 10 of 310 articles

A Framework for Identifying Serum Exosomal Lipid Biomarkers in Alzheimer's Disease.

ACS chemical neuroscience
The escalating global burden of Alzheimer's disease (AD), projected to reach $16.9 trillion by 2050 with disproportionate impacts on low- and middle-income countries and racial minorities, underscores an urgent need for accessible early detection too...

Machine Learning-Driven Extracellular Vesicles Peptidomics Powers Precision Classification of Endometrial Cancer.

Analytical chemistry
Endometrial cancer (EC) molecular subtyping is critical for prognosis and treatment but remains hindered by reliance on invasive tissue biopsies and time-consuming genomic sequencing. Here, we present a minimally invasive approach integrating MALDI-T...

GPMassSimulator: A Graphormer-Based Method for Glycopeptide MS/MS Spectra Prediction.

Analytical chemistry
Protein glycosylation is a critical post-translational modification involved in numerous biological processes and disease states. While mass spectrometry has emerged as the primary tool for glycoproteomics analysis, the structural complexity and hete...

SpecQuality: A Tool for Reliable Spectral Quality Assessment in Proteomics and Proteogenomics.

Journal of the American Society for Mass Spectrometry
Proteogenomics integrates genomics and mass spectrometry (MS) data to understand complex biological systems, disease mechanisms, and potential biomarkers. However, the high volume and noise in MS data present computational and interpretational challe...

Bioactive compound identification without fractionation: an Ocimum spp. case study.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Identifying the phytochemistry underpinning a plant's observed therapeutic benefits is essential for understanding mechanisms of action and developing novel therapeutics. More recent efforts fusing global metabolomics and multivariate p...

Precision integrated identification of predictive first-trimester metabolomics signatures for early detection of gestational diabetes mellitus.

Cardiovascular diabetology
BACKGROUND AND AIM: Gestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, often goes undiagnosed until the second trimester, limiting early intervention opportunities. Given the higher prevalence of GDM in India, there i...

A machine learning framework for classifying lipids in untargeted metabolomics using mass-to-charge ratios and retention times.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: The identification of unknown metabolites remains a major challenge in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS). This process typically depends on comparing mass spectral or chromatographic data to r...

Development of a Novel Hydroxylamine-Based Stable Isotope Labeling Reagent for Profiling Aldehyde Metabolic Biomarkers in Diabetes Using LC-MS/MS and Machine Learning.

Analytical chemistry
Aldehyde compounds are significantly associated with diabetes mellitus. The metabolic profile of aldehydes can enhance understanding of the mechanisms underlying development of diabetes. This study employed a pair of stable isotope labeling (SIL) rea...

The Identification of Biological Stains at Crime Scenes: A Promising Role for Proteomics and Machine Learning.

Analytical chemistry
Forensic body fluid identification is crucial for reconstructing crime scene events. While DNA analysis provides individualization, it lacks information about the fluid's origin. We developed and evaluated three complementary proteomic approaches usi...

DIATAGeR: Triacylglycerol annotation of data-independent acquisition based lipidomics.

Analytica chimica acta
BACKGROUND: Triacylglycerols (TGs) are the most abundant lipids in the human body and the primary source of energy storage. TGs are comprised of three fatty acyls with various lengths and double bond composition, complicating structural annotation wh...