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Quantitative Lipidomics of Biological Samples Using Supercritical Fluid Chromatography Mass Spectrometry.

Methods Mol Biol

January 2025

Division of Metabolomics, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan.

Lipidomics has attracted attention in the discovery of unknown biomolecules and for capturing the changes in metabolism caused by genetic and environmental factors in an unbiased manner. However, obtaining reliable lipidomics data, including structural diversity and quantification data, is still challenging. Supercritical fluid chromatography (SFC) is a suitable technique for separating lipid molecules with high throughput and separation efficiency.

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Urinalysis is one of the predominant tools for clinical testing owing to the abundant composition, sufficient volume, and non-invasive acquisition of urine. As a critical component of routine urinalysis, urine protein testing measures the levels and types of proteins, enabling the early diagnosis of diseases. Traditional methods require three separate steps including strip testing, protein/creatinine ratio measurement, and electrophoresis respectively to achieve qualitative, quantitative, and classification analyses of proteins in urine with long time and cumbersome operations.

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The human hippocampus, essential for learning and memory, is implicated in numerous neurological and psychiatric disorders, each linked to specific neuronal subpopulations. Advancing our understanding of hippocampal function requires computational models grounded in precise quantitative neuronal data. While extensive data exist on the neuronal composition and synaptic architecture of the rodent hippocampus, analogous quantitative data for the human hippocampus remain very limited.

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Two-dimensional liquid chromatography (2D-LC) separation systems, based on two independent columns with different separation mechanisms, have exhibited strong resolving power for complex samples. Therefore, in recent years, the exceptional resolution of 2D-LC has significantly advanced the chemical separation of natural products, such as complex herbs, greatly facilitating their qualitative and quantitative analysis. This paper aims to review the latest strategies of 2D-LC in the quantitative analysis of complex chemical compositions in natural products.

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Adapting Methods for Isolation and Enumeration of Microplastics to Quantify Tire Road Wear Particles with Confirmation by Pyrolysis GC-MS.

Environ Sci Technol

January 2025

U.S. Environmental Protection Agency, E205-02, Research Triangle Park, P.O. Box 12055, Durham, North Carolina 27711, United States.

The complex, varied composition (i.e., rubbers/elastomers, carbon black, fillers, additives, and embedded road materials) and wide density range of tire road wear particles (TRWPs) present challenges for their isolation and identification from environmental matrices.

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