Exploration of Diagnostic Markers Associated with Inflammation in Chronic Kidney Disease Based on WGCNA and Machine Learning.

Chronic kidney disease (CKD) is a common disorder related to inflammatory pathways; its effective management remains limited. This study aimed to use bioinformatics analysis to find diagnostic markers that might be therapeutic targets for CKD. CKD microarray datasets were screened from the GEO database and the differentially expressed genes (DEGs) in CKD dataset GSE98603 were analyzed. Gene set variation analysis (GSVA) was used to explore the activity scores of the inflammatory pathways and samples. Algorithms such as weighted gene co-expression network analysis (WGCNA) and Lasso were used to screen CKD diagnostic markers related to inflammation. Then functional enrichment analysis of inflammation-related DEGs was performed. ROC curves were conducted to examine the diagnostic value of inflammation-related hub-genes. Lastly, quantitative real-time PCR further verified the prediction of bioinformatics. A total of 71 inflammation-related DEGs were obtained, of which 5 were hub genes. Enrichment analysis showed that these genes were significantly enriched in inflammation-related pathways (NF-κB, JAK-STAT, and MAPK signaling pathways). ROC curves showed that the 5 CKD diagnostic markers (TIGD7, ACTA2, ACTG2, MAP4K4, and HOXA11) also exhibited good diagnostic value. In addition, TIGD7, ACTA2, ACTG2, and HOXA11 expression was downregulated while MAP4K4 expression was upregulated in LPS-induced HK-2 cells. The present study identified TIGD7, ACTA2, ACTG2, MAP4K4, and HOXA11 as reliable CKD diagnostic markers, thereby providing a basis for further understanding of CKD in clinical treatments.

Higher dietary acid load is associated with hyperuricemia in Chinese adults: a case-control study.

BACKGROUND: This study aims to explore the association between dietary acid load and hyperuricemia in Chinese adults. METHODS: A case-control study was conducted. Adult participants with hyperuricemia were recruited as the cases and those without hyperuricemia were as the controls. Food consumption was evaluated by food frequency questionnaire (FFQ). Dietary acid load was assessed by potential renal acid load (PRAL) and net endogenous acid production (NEAP). Dietary acid load was divided into four levels: the first quartile (Q1), the second quartile (Q2), the third quartile (Q3) and the fourth quartile (Q4). Logistic regression model was applied for exploring the association between dietary acid load (PRAL and NEAP) and hyperuricemia. Odds ratio (OR) and its correspondence confidence interval (CI) were computed. RESULTS: A total of 290 participants were eligible in this study, in which there were 143 individuals in case group and 147 in control group. A higher level of PRAL was found to be associated with odds of hyperuricemia. ORs of hyperuricemia for Q2, Q3 and Q4 of PRAL were 2.74 (95%CI: 1.94 ~ 3.88, p-value: 0.004), 2.90 (95%CI: 2.05 ~ 4.10, p-value: 0.002) and 3.14 (95%CI: 2.22 ~ 4.45, p-value: 0.001), respectively. There was a positive association between elevated NEAP and hyperuricemia. OR of hyperuricemia for Q2 was not material significance (OR:1.54, 95%CI: 0.93 ~ 2.53, p-value: 0.210), however, ORs of hyperuricemia for Q3 (OR: 2.40, 95%CI: 1.70 ~ 3.38, p-value: 0.011) and Q4 (OR: 3.27, 95%CI: 2.31 ~ 4.62, p-value: 0.001) were statistically significant. CONCLUSION: Higher level of dietary acid load was found to be associated with hyperuricemia in Chinese adults, indicative of advocation of a well-balanced diet in this population.

How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective.

From a technical perspective, for electronic medical records (EMR), this paper proposes an effective confidential management solution on the cloud, whose basic idea is to deploy a trusted local server between the untrusted cloud and each trusted client of a medical information management system, responsible for running an EMR cloud hierarchical storage model and an EMR cloud segmentation query model. (1) The EMR cloud hierarchical storage model is responsible for storing light EMR data items (such as patient basic information) on the local server, while encrypting heavy EMR data items (such as patient medical images) and storing them on the cloud, to ensure the confidentiality of electronic medical records on the cloud. (2) The EMR cloud segmentation query model performs EMR related query operations through the collaborative interaction between the local server and the cloud server, to ensure the accuracy and efficiency of each EMR query statement. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed solution for confidentiality management of electronic medical records on the cloud, i.e., which can ensure the confidentiality of electronic medical records on the untrusted cloud, without compromising the availability of an existing medical information management system.