Research hotspots and trends of tigecycline drug resistance: A study based on CiteSpace / 公共卫生与预防医学

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.

Associations of ambient PM2.5 and O3 with human mortality: A time-series study in a city of central China / 环境与职业医学

Background Exposure to air pollutants O3 and PM2.5 is closely related to population mortality. Most of the domestic research findings are for residents in coastal areas, and less for those in the central and western regions. Objective To investigate the acute effects of O3 and PM2.5 on the mortality of residents in a city of central China. Methods Data were collected on atmospheric pollutants, meteorological data, and population mortality in a city of central China from January 1, 2015 to June 30, 2021. Meteorological data included daily average temperature, air pressure, and relative humidity. Atmospheric pollution data included daily mean concentrations of PM2.5, PM10, SO2, NO2, and CO and maximum 8 h O3. Generalized additive model with Poisson distribution was used for estimating the relationships between air pollutants (O3 and PM2.5) and population mortality, and further stratified by age, gender, and education. Results The daily maximum 8 h average concentration of O3 in the city during the study period was 94.38 μɡ·m−3 and the daily average concentration of PM2.5 was 55.56 μɡ·m−3. In the single-pollutant model, the correlations between O3 concentration and total deaths as well as deaths due to respiratory, circulatory, hypertension, coronary heart disease, and stroke were strongest at lag02, lag2, lag02, lag0, lag02, and lag0, and for every 10 μɡ·m−3 increase in concentration of O3, the associated ER (95%CI) values of daily mortality were increased by 0.09% (−0.08%–0.25%), 0.35% (0–0.71%), 0.43% (0.18%–0.68%), 0.45% (0.02%–0.91%), 0.59% (0.16%–1.02%), and 0.33% (0.01%–0.65%), respectively. The effect of O3 on total mortality was not statistically significant (P>0.05). The correlations between PM2.5 concentration and total deaths, as well as deaths due to respiratory, circulatory, hypertension, coronary heart disease, and stroke were strongest at lag1, lag5, lag01, lag05, lag04, and lag01, and for every 10 μɡ·m−3 increase in concentration of PM2.5, the associated ER (95%CI) values of daily mortality increased by 0.02% (−0.09–0.13%), 0.25% (0.01%–0.50%), 0.35% (0.16%–0.54%), 1.18% (0.59%–1.77%), 0.17% (−0.13%–0.40%), and 0.65% (0.38%–0.92%), respectively, with no statistically significant effects of PM2.5 on total mortality and mortality due to coronary heart disease (P>0.05). During warm season (from May to October), the ER (95%CI) values of total deaths per 10 μɡ·m−3 increase in O3 in male, people aged 6~65 years, people aged >65 years, and people below high school education were 0.46% (0.16%–0.75%), 0.38% (0.08%–0.68%), 0.41% (0.14%–0.66%), and 0.38% (0.14%–0.61%), respectively, while the O3 effect was not statistically significant (P>0.05) during cool season (from November to April). Conclusions Atmospheric pollutants (O3 and PM2.5) have acute effects on mortality in the city, with the elderly, people with less than a high school education, and those with circulatory disease being more sensitive to O3 and PM2.5 exposures.

Predictive and analysis of COVID-19 cases cumulative total：ARIMA model based on machine learning

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.

Meta-analysis of relationship between short-term ozone exposure and population mortality in China / 环境与职业医学

Background In recent years, our country's atmospheric particulate matter pollution has improved significantly, while ozone (O3) pollution has become increasingly serious. As a secondary pollutant, O3 is closely related to human health. Objective To study the effect of short-term exposure to ozone in ambient air on population mortality in China. Methods A computer search with key words of "ozone or O3", "death", and "time series" in Chinese or "ozone", "mortality", and "China" in English was performed in Web of Science, PubMed, China National Knowledge Infrastructure, Wanfang, and VIP databases to find literature on effects of short-term ozone exposure on population mortality covering a time period from January 1, 1990 to December 31, 2021. According to a set of inclusion and exclusion criteria developed for this study, literaturescreening, quality evaluation, andrelevant data extraction were carried out. Finally, R 4.1.2 software was used to perform meta-analysis to estimate target effect sizes. Results A total of 978 articles were retrieved. According to the inclusion and exclusion criteria, 18 articles were finally included, including 39 effect size estimates. The results showed that every 10 μɡ·m−3 increase in ambient ozone concentration was associated with an increase of 0.45% (95%CI: 0.39%-0.51%), 0.50% (95%CI: 0.33%-0.68%), and 0.60% (95%CI: 0.48%-0.72%) in total, respiratory, and cardiovascular disease mortalities , respectively. The results of subgroup analysis by age, sex, and season showed that when ozone concentration increased 10 μɡ·m−3, an increase of 0.34% (95%CI: 0.17%-0.51%) in mortality was observed in the ≥ 65-year-old population, higher than 0.09% (95%CI: −0.21%-0.39%) increase in the <65-year-old population; the mortality increase in females [0.44% (95%CI: 0.30%-0.58%)] was greater than that in males [0.35% (95%CI: 0.22%-0.48%)]; compared with the warm season [0.29% (95%CI: 0.16%-0.42%)], mortality increase was higher in the cold season [1.03% (95%CI: 0.71%-1.35%)]. Conclusion Ambient ozone is an important factor affecting population mortality. The elderly and women ≥ 65 years old in China are more sensitive to ozone, and the impact of ozone exposure on population mortality is greater in cold season.