Adrenal Vein Sampling (AVS) is the gold standard for categorizing primary aldosteronism (PA). However, catheterization of the right adrenal vein (RAV) is challenging due the small size and variable location. This study aims to explore the relationship between the RAV orifice and the right kidney contour (RKC) on fluoroscopy, thus evaluating the potential of use the RKC as an anatomic marker for localizing RAV. Imaging data of 107 PA patients with successful bilateral AVS were retrospectively analyzed. Based on the body mass index (BMI), all patients were divided into the Normal Group (BMI < 24 kg/m2), Overweight Group (24 kg/m2 ≤ BMI < 28 kg/m2) and Obese Group (BMI ≥ 28 kg/m2). At the anterior view, the height level of RAV orifice was determined relative to vertebral bodies and disks. The distance from the RAV orifice to the upper edge of RKC was measured manually. The RAV orifice height level was mainly distributed from vertebral T11 to T12 (90.6%), and tended to be higher in patients with a larger BMI. The mean distance from the RAV orifice to the upper edge of RKC was 13.9±7.8mm, and had no difference among Normal group (n = 53, 14.1±8.2mm), Overweight group (n = 39, 13.7±8.0mm), and Obese group (n = 15, 13.9±5.5mm) (p = 0.981). Based on these findings, the RKC might be used as a landmark for localizing RAV on fluoroscopy, which is conductive to narrow down the exploration range and increase the success rate of RAV catheterization.

Background:COVID-19, the highly contagious respiratory disease, has become a major threat to humanity, and its extrapulmonary effects were also evident. Heart failure (HF) may be the result of myocardial damage associated with COVID-19. Methods:To understand the relationship between SARS-COV-2 and HF, we used bioinformatics analysis to identify common pathways and molecular biomarkers for HF and COVID-19. In this study, two datasets (GSE152418, GSE57338) from Gene Expression Omnibus (GEO) were used to identify differentially expressed genes (DEGs) of SARS-COV-2 infection in HF patients to find common pathways and drug candidates. Results:A total of 123 common DEGs were identified in the two datasets. Using a variety of bioinformatics tools, we first constructed protein-protein interactions (PPI) and then identified hub genes that could be served as potential biomarkers or novel therapeutic strategies. In addition, some common associations between HF and the progression of COVID-19 infection were found by using functional under ontological terms and pathway analysis. Through the datasets, we also identified transcription factor-gene interactions, protein-drug interactions, and co-regulatory network of DEGs-miRNAs with common DEGs. We built gene-disease association network to represent diseases associated with mutual DEGs.Conclusions:Our study has identified the candidate hub genes and drugs that might become a new therapeutic target for novel coronavirus vaccine development and treatment in COVID-19 and HF.

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