ObjectiveTo analyze the clinical and epidemiological characteristics of hospitalized avian influenza A (H7N9) virus infections in Hunan province from 2013 to 2017, and provide evidences for control, diagnosis and treatment of this disease.MethodsNinety-one hospitalized patients were confirmed with H7N9 infection in Hunan. Excluding 2 patients less than 18 years old and 10 with missing data, 79 patients with H7N9 infection were analyzed.ResultsMost confirmed cases were affected in the second and fifth epidemic wave and number of patients in the fifth wave was more than the sum in prior 4 waves. Epidemiological characteristics, clinical symptoms and case fatality did not change significantly. Administration of antiviral drugs was more active in the fifth wave [from illness onset to antiviral drug: (6.3±2.4)d vs. (7.6±2.4)d, P=0.047]. Multiple logistic regression analysis showed that shock (OR=4.683, 95%CI 1.136–19.301, P=0.033) was the independent risk factor of H7N9 infections. There were no significant differences in case fatality among group oseltamivir, group oseltamivir+peramivir, and group peramivir.ConclusionsPatients with avian influenza A (H7N9) increased in the fifth wave but clinical characteristics changed little. Antiviral treatment should be more active. Shock is an independent risk factor of H7N9 infections. Oseltamivir-peramivir biotherapy can not reduce case fatality compared with oseltamivir or peramivir monotherapy.
ObjectiveTo establish the gene-based esophageal cancer (ESCA) risk score prediction models via whole transcriptome analysis to provide ideas and basis for improving ESCA treatment strategies and patient prognosis.MethodsRNA sequencing data of esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC) and adjacent tissues were obtained from The Cancer Genome Atlas database. The edgeR method was used to screen out the differential genes between ESCA tissue and normal tissue, and the key genes affecting the survival status of ESCC and EAC patients were initially identified through univariate Cox regression analysis. The least absolute shrinkage and selection operator regression analysis and multivariate Cox regression analysis were used to further screen genes and establish ESCC and EAC risk score prediction models.ResultsThe risk score prediction models were the independent prognostic factors for ESCA, and the risk score was significantly related to the survival status of patients. In ESCC, the risk score was related to T stage. In EAC, the risk score was related to lymph node metastasis, distant metastasis and clinical stage. The constructed nomogram based on risk score showed good predictive ability. In ESCC, the risk score was related to tumor immune cell infiltration and the expression of immune checkpoint genes. However, this feature was not obvious in EAC.ConclusionThe ESCC and EAC risk score prediction models have shown good predictive capabilities, which provide certain inspiration and basis for optimizing the management of ESCA and improving the prognosis of patients.