Publications by authors named "K A Lippa"
Article Synopsis
- Scientists from 34 labs in 19 countries worked together to measure certain fats (ceramides) in human blood using special techniques.
- They used both standard methods and their own methods to get very accurate and consistent results.
- The study helps improve future medical tests and treatments by providing reliable information about these fats in blood samples.
Article Synopsis
- Different analytical methods in metabolomics and lipidomics include untargeted, targeted, and semi-targeted approaches, with Ultra High Performance Liquid Chromatography-Mass Spectrometry being a key tool due to its efficiency in detecting metabolites.
- The review aims to clarify the differences among these methods in terms of determining metabolite quantities and to discuss their respective advantages and limitations related to accuracy and precision.
- The choice of method is influenced by factors such as prior knowledge of metabolites, the need for peak responses or absolute concentrations, and the desired number of metabolites to analyze, with each method providing different reporting capabilities.
The US National Institute of Standards and Technology (NIST) developed a Standard Reference Material® (SRM®) 3949 Folate Vitamers in Frozen Human Serum to replace SRM 1955 Homocysteine and Folate in Human Serum. The presence of increased endogenous levels of folic acid and 5-methyltetrahydrofolate (5mTHF) in SRM 3949, enhanced folate stability via addition of ascorbic acid, and inclusion of values for additional minor folates are improvements over SRM 1955 that should better serve the clinical folate measurement community. The new SRM contains folates at three levels.
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- Human metabolomics studies typically identify only about 10% of the molecular features present in samples.
- The authors propose a new method that uses reference data to match metabolomics tandem mass spectrometry (MS/MS) data against annotated source data, effectively creating a pseudo-MS/MS library.
- This new approach significantly improves the usage of MS/MS spectra by 5.1 times compared to traditional methods, enabling better analysis of dietary patterns from untargeted metabolomic data.