Bioinformatics identification of potential biomarkers and therapeutic targets for ischemic stroke and vascular dementia.

Ischemic stroke and vascular dementia, as common cerebrovascular diseases, with the former causing irreversible neurological damage and the latter causing cognitive and memory impairment, are closely related and have long received widespread attention. Currently, the potential causative genes of these two diseases have yet to be investigated, and effective early diagnostic tools for the diseases have not yet emerged. In this study, we screened new potential biomarkers and analyzed new therapeutic targets for both diseases from the perspective of immune infiltration. Two gene expression profiles on ischemic stroke and vascular dementia were obtained from the NCBI GEO database, and key genes were identified by LASSO regression and SVM-RFE algorithms, and key genes were analyzed by GO and KEGG enrichment. The CIBERSORT algorithm was applied to the gene expression profile species of the two diseases to quantify the 24 subpopulations of immune cells. Moreover, logistic regression modeling analysis was applied to illustrate the stability of the key genes in the diagnosis. Finally, the key genes were validated using RT-PCR assay. A total of 105 intersecting DEGs genes were obtained in the 2 sets of GEO datasets, and bioinformatics functional analysis of the intersecting DEGs genes showed that GO was mainly involved in the purine ribonucleoside triphosphate metabolic process，respiratory chain complex，DNA-binding transcription factor binding and active transmembrane transporter activity. KEGG is mainly involved in the Oxidative phosphorylation, cAMP signaling pathway. The LASSO regression algorithm and SVM-RFE algorithm finally obtained three genes, GAS2L1, ARHGEF40 and PFKFB3, and the logistic regression prediction model determined that the three genes, GAS2L1 (AUC: 0.882), ARHGEF40 (AUC: 0.867) and PFKFB3 (AUC: 0.869), had good diagnostic performance. Meanwhile, the two disease core genes and immune infiltration were closely related, GAS2L1 and PFKFB3 had the highest positive correlation with macrophage M1 (p < 0.001) and the highest negative correlation with mast cell activation (p = 0.0017); ARHGEF40 had the highest positive correlation with macrophage M1 and B cells naive (p < 0.001), the highest negative correlation with B cell memory highest correlation (p = 0.0047). RT-PCR results showed that the relative mRNA expression levels of GAS2L1, ARHGEF40, and PFKFB3 were significantly elevated in the populations of both disease groups (p < 0.05). Immune infiltration-based models can be used to predict the diagnosis of patients with ischemic stroke and vascular dementia and provide a new perspective on the early diagnosis and treatment of both diseases.

Facile synthesis of spherical covalent organic frameworks for enrichment and quantification of aryl organophosphate esters in mouse serum and tissues.

Here, an imine-linked-based spherical covalent organic framework (COF) was prepared at room temperature. The as-synthesized spherical COF served as an adsorbent in dispersive solid-phase extraction (dSPE), by its virtue of great surface area (1542.68 m2 /g), regular distribution of pore size (2.95 nm), and excellent stability. Therefore, a simple and high-efficiency dispersive solid phase extraction method based on a spherical COF coupled with high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was established to determine aryl organophosphate esters in biological samples. This approach displayed favorable linearity in the range of 10.0-1000.0 ng/L (r > 0.9989), a high signal enhancement factor (58.8-181.8 folds) with low limits of detection (0.3-3.3 ng/L). Moreover, it could effectively eliminate complex matrix interference to accurately extract seven aryl organophosphate esters from mouse serum and tissue samples with spiked recoveries of 82.0%-117.4%. The as-synthesized spherical COF has been successfully applied in sample preparation. The dSPE-HPLC-MS/MS method based on a spherical COF has potential application to study the pollutants' metabolism in vivo.