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With Internet entering all walks of life, development of internet and usage expansion demand better performance, especially the application of 5G network that adopts NAS networking mode. Some of the network bandwidth cannot fully support the current network demand, which causes network fluctuations and other concerns. In this paper, a method for optimizing the topological structure of the bottom layer of the communication network is proposed that has outage performance close to optimal routing scheme. In specific, path in areas with poor network conditions is first optimized using Viterbi algorithm. Then, network element nodes on the path are optimized using Bayes recommendation algorithm for reasonable flow distribution. Dual planning of improved Viterbi algorithm is used to realize the main and standby path planning, and then, Bayesian recommendation algorithm based on the average value is used to optimize the network elements. Therefore, it is very efficient to realize overall performance optimization.

Background and Aims Antineutrophil cytoplasmic autoantibody-associated vasculitis (AAV) is a type of necrotizing small vessel inflammatory disease, including alveolar hemorrhage and rapidly progressive glomerulonephritis. Elderly patients with antineutrophil cytoplasmic autoantibody-associated vasculitis (AAV) commonly experience renal impairment and poor prognosis. Based on the cohort of inpatients with AAV in Peking Union Medical College Hospital, this study aimed to analyze the clinical manifestations, diagnosis and treatment characteristics of elderly patients compared with non-elderly patients, and establish a risk scoring system for predicting composite renal outcomes in patients with AAV in elderly patients. Method This retrospective observational study included all AAV patients hospitalized in a single-center tertiary hospital in China between January 2013 and April 2022. Patients aged ≥ 65 years were defined as elderly and randomly divided into development and validation cohorts (2:1). Logistic regression analysis was performed in the development cohort to analyze risk factors. The scoring system was established accordingly and further validated in the validation cohort. Results A total of 1203 patients were enrolled in the study, among whom the elderly group accounted for 36% with a mean age of 71.4 ± 0.3 years. The elderly group had a worse prognosis, a higher mortality rate (8.9% vs. 3.5%, P < 0.001), a higher rate of end-stage renal disease (17.6% vs. 10.2%, P < 0.001), and worsening renal function (7.4% vs. 4.5%, P = 0.04). Logistic regression showed that age > 75 years, chronic heart disease, elevated serum creatine, and D-dimer values were risk factors for poor prognosis in patients with AAV. The development and validation cohorts in AAV patients produced area under the curve values of 0.823 (0.784–0.862) and 0.833 (0.777–0.888), respectively (Figure 1). When the risk score was ≥ 2 points, the risk of acquiring composite renal outcomes increased significantly with a sensitivity and specificity of 75.0% and 79.9%, respectively. Conclusion We established risk-scoring systems based on baseline clinical characteristics to predict composite renal outcomes in patients with AAV. Our results suggest that more attention should be paid to elderly patients who have severe renal impairment and active inflammation.

Background: Nasopharyngeal carcinoma (NPC) represents a highly aggressive malignant tumor. Competing endogenous RNAs (ceRNA) regulation is a common regulatory mechanism in tumors. The ceRNA network links the functions between mRNAs and ncRNAs, thus playing an important regulatory role in diseases. This study screened the potential key genes in NPC and predicted regulatory mechanisms using bioinformatics analysis.Methods: The merged microarray data of three NPC-related mRNA expression microarrays from the Gene Expression Omnibus (GEO) database and the expression data of tumor samples or normal samples from the nasopharynx and tonsil in The Cancer Genome Atlas (TCGA) database were both subjected to differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The results from two different databases were intersected with WGCNA results to obtain potential regulatory genes in NPC, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. The hub-gene in candidate genes was discerned through Protein-Protein Interaction (PPI) analysis and its upstream regulatory mechanism was predicted by miRwalk and circbank databases.Results: Totally 68 upregulated genes and 96 downregulated genes in NPC were screened through GEO and TCGA. According to WGCNA, the NPC-related modules were screened from GEO and TCGA analysis results, and the genes in the modules were obtained. After the results of differential analysis and WGCNA were intersected, 74 differentially expressed candidate genes associated with NPC were discerned. Finally, fibronectin 1 (FN1) was identified as a hub-gene in NPC. Prediction of upstream regulatory mechanisms of FN1 suggested that FN1 may be regulated by ceRNA mechanisms involving multiple circRNAs, thereby influencing NPC progression through ceRNA regulation.Conclusion: FN1 is identified as a key regulator in NPC development and is likely to be regulated by numerous circRNA-mediated ceRNA mechanisms.