ABSTRACT
Objective To explore the research progress, research hotspot and development trend of tigecycline resistance based on the quantitative analysis and visualization function of CiteSpace. Methods The data were collected from 4,263 Chinese and English articles on tigecycline resistance in CNKI, Wanfang, VIP and Web of Science (WOS) databases from 2012 to 2022. CiteSpace 5.8.R3 software was used to analyze the cooperative network of authors, the cooperative network of countries and institutions, the total citation times of journals, and keywords included in the literature, to reveal the hotspots and trends of tigecycline resistance research. Results The number of articles published in English literature was higher than that in Chinese literature. China had the largest number of published documents, showing a significant international academic influence in this research field. Countries all over the world were concerned about the resistance of tigecycline, but Chinese literatures focused more on the clinical infection and prevention of tigecycline resistance, while English literatures placed special emphasis on the research about the drug resistance mechanism of tigecycline. Conclusion The research direction at home and abroad is basically the same, but the research focus has gradually shifted from the clinical treatment and monitoring of tigecycline to the molecular level of drug resistance mechanism.
ABSTRACT
Objective In this study, a strain of colistin and tigecycline-resistant bacteria isolated in 2009 was analyzed, and the structure of drug-resistant plasmid and genetic environment were discussed, so as to provide basis for the prevention and control of multidrug-resistant bacteria. Methods A strain (GZ12244) with positive mcr and tet(M) was obtained by screening colistin and tigecycline resistance genes. Vitek-2 was used for strain identification, and the drug sensitivity test was carried out by broth dilution method. The molecular typing, drug resistance genes, insertion sequences, plasmid structure and genetic background were analyzed by genome-wide sequencing and bioinformatics. Results Strain GZ12244 is Klebsiella pneumoniae, which is resistant to colistin B, tigecycline, cefuroxime and tetracycline, and carries a variety of drug-resistant related genes such as mcr-1 and tet(M), and some of the drug-resistant genes with antibiotic efflux and antibiotic target change have amino acid substitution mutations. Mcr-1 and tet(M) coexist in a plasmid, and mcr-1 flanked by two insertion sequences ISApl1. There are insertion sequences such as IS15, IS1D and ISEc63 in the upstream and downstream of tet(M) gene. Conclusion Klebsiella pneumoniae GZ12244 is a multidrug-resistant strain. The drug-resistant gene exists in plasmid, and the mobile elements in upstream and downstream may spread the drug-resistant gene.
ABSTRACT
At present, COVID-19 poses a serious threat to global human health, and the cumulative confirmed cases in America, Brazil and India continue to grow rapidly. Therefore, the prediction models of cumulative confirmed cases in America, Brazil and India from August 1, 2021 to December 31, 2021 were established. In this study, the prevalence data of COVID-19 from 1 August 2021 to 31 December 2021 were collected from the World Health Organization website. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (7,2,0), ARIMA (3,2,1), and ARIMA (10,2,4) models were selected as the best models for America, Brazil, and India, respectively. Initial combinations of model parameters were selected using the automated ARIMA model, and the optimized model parameters were then found based on Bayesian information criterion (BIC). The analytical tools autocorrelation function (ACF), and partial autocorrelation function (PACF) were used to evaluate the reliability of the model. The performance of different models in predicting confirmed cases from January 1, 2022 to January 5, 2022 was compared by using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). This study shows that ARIMA models are suitable for predicting the prevalence of COVID-19 in the future. The results of the analysis can shed light on understanding the trends of the outbreak and give an idea of the epidemiological stage of these regions. Besides, the prediction of COVID-19 prevalence trends of America, Brazil, and India can help take precautions and policy formulation for this epidemic in other countries.