Background. Atherosclerotic plaque instability is a common cause of stroke and ischemic infarction, and identification of monocyte-associated genes has become a prominent feature in cardiovascular research as a contributing/predictive marker. Methods. Whole genome sequencing data were downloaded from GSE159677, GSE41571, GSE120521, and GSE118481. Single-cell sequencing data analysis was conducted to cluster molecular subtypes of atherosclerotic plaques and identify specific genes. Differentially expressed genes (DEGs) between normal subjects and patients with unstable atheromatous plaques were screened. Weighted gene coexpression network analysis (WGCNA) was performed to find key module genes. In addition, GO and KEGG enrichment analyses explored potential biological signaling pathways to generate protein interaction (PPI) networks. GSEA and GSVA demonstrated activations in plaque instability subtypes. Results. 239 monocyte-associated genes were identified based on bulk and single-cell RNA-sequencing, followed by the recognition of 1221 atherosclerotic plaque-associated DEGs from the pooled matrix. GO and KEGG analyses suggested that DEGs might be related to inflammation response and the PI3K-Akt signaling pathway. Eight no-grey modules were obtained through WGCNA analysis, and the turquoise module has the highest correlation with unstable plaque ( R 2 = 0.40 ), which contained 1323 module genes. After fetching the intersecting genes, CXCL3, FPR1, GK, and LST1 were obtained that were significantly associated with plaque instability, which had an intense specific interaction. Monocyte-associated genes associated with atherosclerotic plaque instability have certain diagnostic significance and are generally overexpressed in this patient population. In addition, 11 overlapping coexpressed genes (CEG) might also activated multiple pathways regulating inflammatory responses, platelet activation, and hypoxia-inducible factors. GSVA showed that the corresponding pathways were significantly activated in high expression samples. Conclusions. Overexpression of CXCL3, GK, FPR1, and LST1 was advanced recognition and intervention factors for unstable plaques, which might become targets for atherosclerosis rupture prevention. We also analyzed the potential mechanisms of CEG from inflammatory and oxidative stress pathways.

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Background To investigate the imaging anatomy and variations of bilateral adrenal veins to improve the success rate during adrenal venous sampling (AVS) and reduce the incidence of complications. Methods A total of 120 patients who underwent AVS from June 2017 to January 2019 were collected. RadiAnt Viewer software was used to retrospectively analyze the intraoperative imaging data, intraoperative anatomical variation data, the success rate, and complications of AVS. Results The ostium of the right adrenal vein was located mainly between the lower 1/3 of the 11th thoracic vertebra and the middle 1/3 of the 12th thoracic vertebra, accounting for 75.5% of the cases. Most of the ostium (83.3%) was transversely distributed between 9 o’clock and 12 o’clock. The main morphology of the right adrenal venography was a triangular pattern (48.2%). As the body mass index increased, the ostium was higher, and the distance between the ostium and the spine was greater (P < 0.05). The success rate of the right AVS, the left AVS, and the bilateral AVS was 95.0%, 97.5%, and 92.5%, respectively. The anatomical variation rate of the right adrenal vein was 5.3%. All cases showed that the right adrenal vein entered the accessory right hepatic vein and then into the inferior vena cava. The anatomical variation rate of the left adrenal vein was 4.3%. Conclusions Body mass index can be used to predict the location of the right adrenal vein ostium. Understanding of the anatomy and variation of the adrenal vein and right adrenal venography is essential to a successful AVS.