Bibliometric analysis of research on retinoic acid in the field of kidney disorders.

Retinoic acid is an active metabolite with significant physiological functions in human development, immunity, vision, and skin health. In recent years, research on retinoic acid in the field of kidney disorders has been increasing gradually. Yet, there is a lack of systematic bibliometric analysis of retinoic acid research in the kidney domain. This study included 1,368 articles published between 1998 and 2023 on treating kidney diseases with retinoic acid. Using the bibliometric analysis software VOSviewer and CiteSpace, we analyzed data on publication trends, contributing countries and institutions, journals and cocited journals, authors and cocited authors, cocited references, research hotspots, and frontiers. On the basis of the results of the bibliometric analysis, we identified the research efforts and their developmental trends, providing the groundwork for future research on retinoic acid.

Research progress of astragaloside IV in treating acute kidney injury.

Acute kidney injury (AKI) is one of the most common clinical critical illnesses, with decreased glomerular filtration rate, retention of nitrogen products, water and electrolyte disorders, and acid-base imbalance as the main clinical manifestations. Presently, there is no effective treatment for acute kidney injury, but the main treatment is to cure the primary disease, remove risk factors, maintain acid-base and water-electrolyte balance, and undergo kidney replacement. However, the mortality rate is still high. Investigations and studies showed that the mortality rate of patients with acute kidney injury in the ICU is 5-80% [1]. In recent years, Chinese medicine has been widely used in acute kidney injury treatment due to its complete dialectical system and rich experience. Astragalus is a commonly used medicine in traditional Chinese medicine to treat acute kidney injury. Astragaloside IV is the main active component of traditional Chinese medicine, Astragalus membranaceus. This article summarizes the relevant studies on treating acute kidney injury with astragaloside IV.

Machine-learning algorithm-based prediction of a diagnostic model based on oxidative stress-related genes involved in immune infiltration in diabetic nephropathy patients.

Diabetic nephropathy (DN) is the most prevalent microvascular consequence of diabetes and has recently risen to the position of the world's second biggest cause of end-stage renal diseases. Growing studies suggest that oxidative stress (OS) responses are connected to the advancement of DN. This study aimed to developed a novel diagnostic model based on OS-related genes. The differentially expressed oxidative stress-related genes (DE-OSRGs) experiments required two human gene expression datasets, which were given by the GEO database (GSE30528 and GSE96804, respectively). The potential diagnostic genes were identified using the SVM-RFE assays and the LASSO regression model. CIBERSORT was used to determine the compositional patterns of the 22 different kinds of immune cell fraction seen in DN. These estimates were based on the combined cohorts. DN serum samples and normal samples were both subjected to RT-PCR in order to investigate the degree to which certain genes were expressed. In this study, we were able to locate 774 DE-OSRGs in DN. The three marker genes (DUSP1, PRDX6 and S100A8) were discovered via machine learning on two different machines. The high diagnostic value was validated by ROC tests, which focused on distinguishing DN samples from normal samples. The results of the CIBERSORT study suggested that DUSP1, PRDX6, and S100A8 may be associated to the alterations that occur in the immunological microenvironment of DN patients. Besides, the results of RT-PCR indicated that the expression of DUSP1, PRDX6, and S100A8 was much lower in DN serum samples compared normal serum samples. The diagnostic value of the proposed model was likewise verified in our cohort, with an area under the curve of 9.946. Overall, DUSP1, PRDX6, and S100A8 were identified to be the three diagnostic characteristic genes of DN. It's possible that combining these genes will be effective in diagnosing DN and determining the extent of immune cell infiltration.