Idiopathic pulmonary fibrosis is a chronic and irreversible respiratory disease with a high incidence worldwide and no specific treatment. Currently, the etiology and pathogenesis of this disease remain largely unknown. In main purpose of this study, bioinformatics analysis was used to uncover key genes and pathways related to idiopathic pulmonary fibrosis (IPF). Gene expression profiles of GSE2052 and GSE35145 were obtained. After combining the 2 chip groups; then, we normalized the data, eliminating batch difference. R software was used to process and to screen differentially expressed genes (DEGs) between the IPF and normal tissues. Then, functional enrichment analysis of these DEGs was carried out, and a protein-protein interaction network (PPI) was also constructed. A total of 276 DEGs (152 up and 134 down-regulated genes) were identified in the IPF lung samples. The PPI network was established with 227 nodes and 763 edges. The top 10 hub genes were CAM1, CDH1, CXCL12, JUN, CTGF, SERPINE1, CXCL1, EDN1, COL1A2, and SPARC . Analyzing the PPI network modules with close interaction, the 3 key modules in the whole PPI network were screened out. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for the module containing DEGs contained the viral protein interaction with cytokine and the cytokine receptor, the TNF signaling pathway, and the chemokine signaling pathway. The identified key genes and pathways may play an important role in the occurrence and development of IPF, and may be expected to be biomarkers or therapeutic targets for the diagnosis of IPF.

Auricular therapy (AT) is a conventional therapy in traditional Chinese medicine. However, the effectiveness of perioperative AT in pain treatment after total hip arthroplasty (THA) is still controversial. Nine randomised controlled trials (RCTs) involving 605 patients who have undergone THA with or without AT from inception to March 2018 were collected and included in this study by searching more than 12 databases (e.g., PubMed, Excerpta Medica Database, and Cochrane Library). A random-effects model that pooled seven articles showed that the visual analogue scale (VAS) in the AT group was lower than that of the control group at each postoperative time point in patients after THA, except at the time points of 6 and 36 h. The intraoperative body mass-adjusted fentanyl amount in the AT group was also lower than that of the control group in two trials. The other outcomes (time to first analgesic request and incidence of postoperative nausea and vomiting, perioperative bradycardia, and transitory hypotension) showed insignificant difference. Then, subgroup analysis showed similar results to those of the total articles with the term “VAS”. Regression analysis found that the prolonged time after the operation decreased the difference in VAS between the two groups. Although all the outcomes were assessed as very low to low in the GRADE system, evidence on the effectiveness of perioperative AT in pain treatment after total hip replacement was positive.

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Background: Chronic cerebral circulation insufficiency (CCCI) is a common clinical cerebrovascular disease, especially among middle-aged and elderly patients, which seriously endangers their quality of life and physical and mental health. At present, Oral traditional Chinese patent medicine (OTCPM) is widely used in the treatment of CCCI in China, but its actual efficacy and safety lack of evidence-based evidence. Therefore, we will screen out the most effective OTCPM through a systematic review and network meta-analysis to provide a reliable theoretical basis for clinical decisions. Methods: We will search electronic databases to collect relevant RCT studies from inception to October 2019. Those electronic databases include PubMed, Cochrane Library, Web of Science, EMBASE, China Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), Chinese Scientific Journal Database (VIP), and Wan-fang database. Only randomized clinical trials (RCTs) concerned any OTCPM treatments for CCCI will be collected. The included studies will no restrictions on language or publication year. There were no publication year or language for the included literature. Risk bias tools will assess the quality of the included literature. A Bayesian NMA will be performed to combine the direct and indirect comparisons of TCPMs interventions. The surface under the cumulative ranking curve (SUCRA) will be drawn to display the hierarchy of each TCPMs treatment. All statistical analyses will be implemented using R v3.5.2. and GeMTC v1.4.3. We will publish this systematic review in academic journals. Since this literature review will not involve directly contacting patients, ethical approval and informed consent are not required. Trial registration number: CRD42019123878.

To better understand the molecular mechanism underlying the pathogenesis of multiple sclerosis (MS), we aimed to identify the key genes and microRNAs (miRNA) associated with MS and analyze their interactions. Differentially expressed genes (DEGs) and miRNAs (DEMs) based on the gene miRNA dataset GSE17846 and mRNA dataset GSE21942 were determined using R software. Next, we performed functional enrichment analysis and constructed a protein–protein interaction network. Data validation was performed to ensure the reliability of hub genes. The miRNA-mRNA regulatory network was constructed. In total, 47 DEMs and 843 DEGs were identified. Protein–protein interaction network analysis identified several hub genes, including JUN, FPR2, AKT1, POLR2L, LYZ, CXCL8, HBB, CST3, CTSZ, and MMP9 , especially LYZ and CXCL8 . We constructed an miRNA-mRNA regulatory network and found that hsa-miR-142-3p, hsa-miR-107, hsa-miR-140-5p, and hsa-miR-613 were the most important miRNAs. This study reveals some key genes and miRNAs that may be involved in the pathogenesis of MS, providing potential targets for the diagnosis and treatment of MS.

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Pancreatic cancer, a common digestive system malignancy, is dubbed the “king of cancers”. The role of pyrophosis-related genes (PRGs) in pancreatic cancer prognosis is yet unknown. In pancreatic cancer and normal tissue, we discovered 9 PRGs that are expressed differently in pancreatic cancer and healthy tissue. Based on the differential expression of PRGs, 2 clusters of pancreatic cancer cases could be identified. The 2 groups had significant disparities in total survival time. The prognostic model of a 5-PRGs signature was created using least absolute shrinkage and selection operator (LASSO) method. The median risk score was used to split pancreatic cancer patients in The Cancer Genome Atlas (TCGA) cohort into 2 groups: low risk and high risk. Patients classified as low-risk had significantly higher survival rates than those classified as high-risk ( P < .01). The same results were obtained by validating them against the Gene Expression Omnibus database ( P = .030). Cox regression statistical analysis showed that risk score was an independent predictor of overall survival in pancreatic cancer patients. Functional enrichment analysis revealed that apoptosis, cell proliferation, and cell cycle-related biological processes and signaling pathways were enriched. Additionally, the immunological status of the high-risk group worsened. In conclusion, a novel pyroptosis-related gene signature can be used to predict pancreatic cancer patient prognosis.