Prediction of hyperuricemia in people taking low-dose aspirin using a machine learning algorithm: a cross-sectional study of the National Health and Nutrition Examination Survey

Background: Hyperuricemia is a serious health problem related to not only gout but also cardiovascular diseases (CVDs). Low-dose aspirin was reported to inhibit uric acid excretion, which leads to hyperuricemia. To decrease hyperuricemia-related CVD, this study aimed to identify the risk of hyperuricemia in people taking aspirin.Method: The original data of this cross-sectional study were obtained from the National Health and Nutrition Examination Survey between 2011 and 2018. Participants who filled in the “Preventive Aspirin Use” questionnaire with a positive answer were included in the analysis. Six machine learning algorithms were screened, and eXtreme Gradient Boosting (XGBoost) was employed to establish a model to predict the risk of hyperuricemia.Results: A total of 805 participants were enrolled in the final analysis, of which 190 participants had hyperuricemia. The participants were divided into a training set and testing set at a ratio of 8:2. The area under the curve for the training set was 0.864 and for the testing set was 0.811. The SHapley Additive exPlanations (SHAP) method was used to evaluate the performances of the modeling. Based on the SHAP results, the feature ranking interpretation showed that the estimated glomerular filtration rate, body mass index, and waist circumference were the three most important features for hyperuricemia in individuals taking aspirin. In addition, triglyceride, hypertension, total cholesterol, high-density lipoprotein, low-density lipoprotein, age, race, and smoking were also correlated with the development of hyperuricemia.Conclusion: A predictive model established by XGBoost algorithms can potentially help clinicians make an early detection of hyperuricemia risk in people taking low-dose aspirin

Screening and identification of key chromatin regulator biomarkers for ankylosing spondylitis and drug prediction: evidence from bioinformatics analysis

Abstract Background Ankylosing spondylitis (AS) is one of the most common immune-mediated arthritic diseases worldwide. Despite considerable efforts to elucidate its pathogenesis, the molecular mechanisms underlying AS are still not fully understood. Methods To identify candidate genes involved in AS progression, the researchers downloaded the microarray dataset GSE25101 from the Gene Expression Omnibus (GEO) database. They identified differentially expressed genes (DEGs) and functionally enriched them for analysis. They also constructed a protein–protein interaction network (PPI) using STRING and performed cytoHubba modular analysis, immune cell and immune function analysis, functional analysis and drug prediction.The results showed that DEGs were mainly associated with histone modifications, chromatin organisation, transcriptional coregulator activity, transcriptional co-activator activity, histone acetyltransferase complexes and protein acetyltransferase complexes. Results The researchers analysed the differences in expression between the CONTROL and TREAT groups in terms of immunity to determine their effect on TNF-α secretion. By obtaining hub genes, they predicted two therapeutic agents, AY 11–7082 and myricetin. Conclusion The DEGs, hub genes and predicted drugs identified in this study contribute to our understanding of the molecular mechanisms underlying the onset and progression of AS. They also provide candidate targets for the diagnosis and treatment of AS

A Visual Discrimination of Existing States of Virus Capsid Protein by a Giant Molybdate Cluster

We report a unique phenomenon, the opposite color response of a giant polyoxometalate, (NH4)42[Mo132O372(CHCOO)30] (H2O)72 ([Mo132]), to the existing states of human papillomavirus (HPV) major capsid protein, L1-pentamer (L1-p), and virus-like particles (VLPs). The color responses originate from the different assembly forms between [Mo132] and the capsid protein. The latter were inspected and separated by using CsCl gradient centrifugation, and validated in detail by sodium dodecyl sulfate-polyacrylamide gel-electrophoresis (SDS-PAGE), dynamic light scattering (DLS), and transmission electron microscopy (TEM) imaging. Furthermore, the intrinsic mechanisms were investigated in-depth by using XPS-based semi-quantitative analysis and well-designed peptides, revealing the critical points of L1 that determine the charge–transfer ratio between Mo(V) to Mo(VI), and consequently, the levels of [Mo132] hypochromic in different assemblies. Such a unique phenomenon is significant as it supplies a colorimetry approach to distinguish the existing states of the HPV capsid protein and would be significant in the quality assay of the HPV vaccine and existing states of other viruses in the future

Expression profiling of hepatic genes associated with lipid metabolism in nephrotic rats

Hyperlipidemia is one of the major features of nephrotic syndrome (NS). Although many factors have been implicated in the pathogenesis of NS-related dyslipidemia, the underlying mechanisms remain largely uncharacterized. The present study was designed to examine the gene profile associated with lipid metabolism in the livers of nephrotic rats. NS was created in male Sprague-Dawley rats (n = 6) receiving sequential intraperitoneal injections of puromycin aminonucleoside. Analysis by Affymetrix assay, quantitative RT-PCR, and Northern and Western blotting revealed 21 genes associated with cholesterol and fatty acid metabolism. Eight genes involved in cholesterol metabolism, Apo A-I, Acly, Acat, Mpd, Fdps, Ss, Lss, and Nsdhl, were significantly upregulated under NS. Four genes involved in fatty acid biosynthesis, Acc, FAS, ELOVL 2, and ELOVL6, and three critical for triglyceride biosynthesis, Gpam, Agpat 3, and Dgat 1, were significantly upregulated, whereas two genes involved in fatty acid oxidation, Dci and MCAD, were downregulated. Expression of several genes in sterol-regulatory element-binding protein (SREBP)-1 activation was also aberrantly altered in nephrotic livers. The expression and transcriptional activity of SREBP-1 but not SREBP-2 were increased in nephrotic rats as assessed by real-time PCR, immunoblotting, and gel shift assays. The upregulation of hepatic genes involved in cholesterol biosynthesis may play an important role in the pathogenesis of hypercholesterolemia, whereas upregulation of genes participating in hepatic fatty acid and triglyceride biosynthesis and downregulation of genes involved in hepatic fatty acid oxidation may contribute to hypertriglyceridemia in nephrotic rats. Activation of SREBP-1 transcription factor may represent an underlying molecular mechanism of hyperlipidemia in NS