Integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke

Mass Spectrometry Imaging as a New Method: To Reveal the Pathogenesis and the Mechanism of Traditional Medicine in Cerebral Ischemia

Mass spectrometry imaging (MSI) can describe the spatial distribution of molecules in various complex biological samples, such as metabolites, lipids, peptides and proteins in a comprehensive way, and can provide highly relevant supplementary information when combined with other molecular imaging techniques and chromatography techniques, so it has been used more and more widely in biomedical research. The application of mass spectrometry imaging in neuroscience is developing. It is very advantageous and necessary to use MSI to study various pathophysiological processes involved in brain injury and functional recovery during cerebral ischemia. Therefore, this paper introduces the techniques of mass spectrometry, including the principle of mass spectrometry, the acquisition and preparation of imaging samples, the commonly used ionization techniques, and the optimization of the current applied methodology. Furthermore, the research on the mechanism of cerebral ischemia by mass spectrometry was reviewed, such as phosphatidylcholine involved, dopamine, spatial distribution and level changes of physiological substances such as ATP in the Krebs cycle; The characteristics of mass spectrometry imaging as one of the methods of metabolomics in screening biomarkers related to cerebral ischemia were analyzed the advantages of MSI in revealing drug distribution and the mechanism of traditional drugs were summarized, and the existing problems of MSI were also analyzed and relevant suggestions were put forward.

EGR1 and KLF4 as Diagnostic Markers for Abdominal Aortic Aneurysm and Associated With Immune Infiltration

Background

Formation and rupture of abdominal aortic aneurysm (AAA) is fatal, and the pathological processes and molecular mechanisms underlying its formation and development are unclear. Perivascular adipose tissue (PVAT) has attracted extensive attention as a newly defined secretory organ, and we aim to explore the potential association between PVAT and AAA.

Methods

We analyzed gene expression and clinical data of 30 PVAT around AAA and 30 PVAT around normal abdominal aorta (NAA). The diagnostic markers and immune cell infiltration of PVAT were further investigated by WGCNA, CIBERSORT, PPI, and multiple machine learning algorisms (including LASSO, RF, and SVM). Subsequently, eight-week-old C57BL/6 male mice (n = 10) were used to construct AAA models, and aorta samples were collected for molecular validation. Meanwhile, fifty-five peripheral venous blood samples from patients (AAA vs. normal: 40:15) in our hospital were used as an inhouse cohort to validate the diagnostic markers by qRT-PCR. The diagnostic efficacy of biomarkers was assessed by receiver operating characteristic (ROC) curve, area under the ROC (AUC), and concordance index (C-index).

Results

A total of 75 genes in the Grey60 module were identified by WGCNA. To select the genes most associated with PVAT in the grey60 module, three algorithms (including LASSO, RF, and SVM) and PPI were applied. EGR1 and KLF4 were identified as diagnostic markers of PVAT, with high accurate AUCs of 0.916, 0.926, and 0.948 (combined two markers). Additionally, the two biomarkers also displayed accurate diagnostic efficacy in the mice and inhouse cohorts, with AUCs and C-indexes all >0.8. Compared with the NAA group, PVAT around AAA was more abundant in multiple immune cell infiltration. Ultimately, the immune-related analysis revealed that EGR1 and KLF4 were associated with mast cells, T cells, and plasma cells.

Conclusion

EGR1 and KLF4 were diagnostic markers of PVAT around AAA and associated with multiple immune cells.

Artificial Intelligence in Chemistry: Current Trends and Future Directions

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.

Analysis of Lipid Peroxidation by UPLC-MS/MS and Retinoprotective Effects of the Natural Polyphenol Pterostilbene

The loss of redox homeostasis induced by hyperglycemia is an early sign and key factor in the development of diabetic retinopathy. Due to the high level of long-chain polyunsaturated fatty acids, diabetic retina is highly susceptible to lipid peroxidation, source of pathophysiological alterations in diabetic retinopathy. Previous studies have shown that pterostilbene, a natural antioxidant polyphenol, is an effective therapy against diabetic retinopathy development, although its protective effects on lipid peroxidation are not well known. Plasma, urine and retinas from diabetic rabbits, control and diabetic rabbits treated daily with pterostilbene were analyzed. Lipid peroxidation was evaluated through the determination of derivatives from arachidonic, adrenic and docosahexaenoic acids by ultra-performance liquid chromatography coupled with tandem mass spectrometry. Diabetes increased lipid peroxidation in retina, plasma and urine samples and pterostilbene treatment restored control values, showing its ability to prevent early and main alterations in the development of diabetic retinopathy. Through our study, we are able to propose the use of a derivative of adrenic acid, 17(RS)-10-epi-SC-Δ¹⁵-11-dihomo-IsoF, for the first time, as a suitable biomarker of diabetic retinopathy in plasmas or urine.