Lag effect of ambient temperature on respiratory emergency department visits in Beijing: a time series and pooled analysis.

BACKGROUND: Although the association between ambient temperature and mortality of respiratory diseases was numerously documented, the association between various ambient temperature levels and respiratory emergency department (ED) visits has not been well studied. A recent investigation of the association between respiratory ED visits and various levels of ambient temperature was conducted in Beijing, China. METHODS: Daily meteorological data, air pollution data, and respiratory ED visits data from 2017 to 2018 were collected in Beijing. The relationship between ambient temperature and respiratory ED visits was explored using a distributed lagged nonlinear model (DLNM). Then we performed subgroup analysis based on age and gender. Finally, meta-analysis was utilized to aggregate the total influence of ambient temperature on respiratory ED visits across China. RESULTS: The single-day lag risk for extreme cold peaked at a relative risk (RR) of 1.048 [95% confidence interval (CI): 1.009, 1.088] at a lag of 21 days, with a long lag effect. As for the single-day lag risk for extreme hot, a short lag effect was shown at a lag of 7 days with an RR of 1.076 (95% CI: 1.038, 1.114). The cumulative lagged effects of both hot and cold effects peaked at lag 0-21 days, with a cumulative risk of the onset of 3.690 (95% CI: 2.133, 6.382) and 1.641 (95% CI: 1.284, 2.098), respectively, with stronger impact on the hot. Additionally, the elderly were more sensitive to ambient temperature. The males were more susceptible to hot weather than the females. A longer cold temperature lag effect was found in females. Compared with the meta-analysis, a pooled effect of ambient temperature was consistent in general. In the subgroup analysis, a significant difference was found by gender. CONCLUSIONS: Temperature level, age-specific, and gender-specific effects between ambient temperature and the number of ED visits provide information on early warning measures for the prevention and control of respiratory diseases.

Mapping knowledge landscapes and emerging trends of Marburg virus: A text-mining study.

Background: Marburg virus (MARV), a close relative of Ebola virus, could induce hemorrhagic fevers in humans with high mortality rate. In recent years, increasing attention has been paid to this highly lethal virus due to sporadic outbreaks observed in various African nations. This bibliometric analysis endeavors to elucidate the trends, dynamics, and focal points of knowledge that have delineated the landscape of research concerning MARV. Methods: Relevant literature on MARV from 1968 to 2023 was extracted from the Web of Science Core Collection database. Following this, the data underwent bibliometric analysis and visualization procedures utilizing online analysis platform, CiteSpace 6.2R6, and VOSviewer 1.6.20. Three different types of bibliometric indicators including quantitative indicator, qualitative indicators, and structural indicators were used to gauge a researcher's productivity, assess the quality of their work, and analyze publication relationships, respectively. Results: MARV is mainly prevalent in Africa. And approximately 643 confirmed cases have been described in the literature to date, and mortality observed was 81.2 % in overall patients. A total of 1014 papers comprising 869 articles and 145 reviews were included. The annual publications showed an increasing growth pattern from 1968 to 2023 (R2 = 0.8838). The United States stands at the forefront of this discipline, having dedicated substantial financial and human resources to scientific inquiry. However, co-authorship analysis showed the international research collaboration needs to be further strengthened. Based on reference and keywords analysis, contemporary MARV research encompasses pivotal areas: primarily, prioritizing the creation of prophylactic vaccines to impede viral spread, and secondarily, exploring targeted antiviral strategies, including small-molecule antivirals or MARV-specific monoclonal antibodies. Additionally, a comprehensive grasp of viral transmission, transcription, and replication mechanisms remains a central focus in ongoing investigations. And future MARV studies are expected to focus on evaluating clinical trial safety and efficacy, developing inhibitors to contain viral spread, exploring vaccine immunogenicity, virus-host association studies, and elucidating the role of neutralizing antibodies in MARV treatment. Conclusion: The present study offered comprehensive insights into the contemporary status and trajectories of MARV over the past decades. This enables researchers to discern novel collaborative prospects, institutional partnerships, emerging topics, and research forefronts within this domain.

Effect of short-term exposure to ambient air pollutants on non-accidental mortality in emergency department visits: a time-series study.

Objectives: Exposure to air pollution has been linked to an increased risk of premature mortality. However, the acute effects of air pollution on the risk of non-accidental mortality have not been extensively researched in developing countries, and the findings thus far have been inconsistent. Therefore, this study aimed to examine the association between short-term exposure to six pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) and non-accidental mortality in Beijing, China. Methods: Daily data on non-accidental deaths were gathered from 1 January 2017 to 31 December 2018. Air pollution data for the same period were collected from 35 fixed-site air quality monitoring stations in Beijing. Generalized additive models (GAM) based on Poisson regression were used to investigate the association between non-accidental mortality in emergency department visits and the daily average levels of air pollutants. Results: There were 8,676 non-accidental deaths recorded during 2017-2018. After sensitivity analysis, short-term exposure to air pollutants, particularly gaseous pollutants, was linked to non-accidental mortality. Specifically, for every 10 µg/m3 increase (5 µg/m3 in SO2, 0.5 mg/m3 in CO) of SO2 (lag 04), NO2 (lag 04), O3 (lag 05), and CO (lag 04), the relative risk (RR) values were 1.054 (95% CI: 1.009, 1.100), 1.038 (95% CI: 1.013, 1.063), 1.032 (95% CI: 1.011, 1.054), and 1.034 (95% CI: 1.004, 1.066), respectively. In terms of causes of death, short-term exposure to NO2, SO2, and O3 increased the risk of circulatory mortality. Further stratified analysis revealed that the stronger associations were presented in females for O3 while in males for CO. People aged 65 and over were strongly associated with ambient air pollution. Conclusions: Our study showed that ambient air pollutants were associated with non-accidental mortality. Our findings suggested that efforts to control gaseous pollution should be stepped up, and vulnerable groups should be the focus of health protection education.

Metformin Mitigates Sepsis-Related Neuroinflammation via Modulating Gut Microbiota and Metabolites.

Gut microbiota affects the functions of brains. However, its mechanism in sepsis remains unclear. This study evaluated the effect of metformin on ameliorating sepsis-related neurodamage by regulating gut microbiota and metabolites in septic rats. Cecal ligation and puncture (CLP) was used to establish the sepsis-related neurodamage animal models. Metformin therapy by gavage at 1 h after CLP administration was followed by fecal microbiota transplantation (FMT) to ensure the efficacy and safety of metformin on the sepsis-related neurodamage by regulating gut microbiota. The gut microbiota and metabolites were conducted by 16S rRNA sequencing and liquid chromatography-tandem mass spectrometry metabolomic analysis. The brain tissue inflammation response was analyzed by histopathology and reverse transcription-polymerase chain reaction (RT-PCR). This study reported brain inflammatory response, hemorrhage in sepsis-related neurodamage rats compared with the control group (C group). Surprisingly, the abundance of gut microbiota slightly increased in sepsis-related neurodamage rats than C group. The ratio of Firmicutes/Bacteroidetes was significantly increased in the CLP group than the C group. However, no difference was observed between the CLP and the metformin-treated rats (MET group). Interestingly, the abundance of Escherichia_Shigella increased in the MET group than the C and CLP groups, while Lactobacillaceae abundance decreased. Furthermore, Prevotella_9, Muribaculaceae, and Alloprevotella related to short-chain fatty acids production increased in the sepsis-related neurodamage of metformin-treated rats. Additionally, Prevotella_9 and Muribaculaceae correlated positively to 29 metabolites that might affect the inflammatory factors in the brain. The FMT assay showed that metformin improved sepsis-related neurodamage by regulating the gut microbiota and metabolites in septic rats. The findings suggest that metformin improves the sepsis-related neurodamage through modulating the gut microbiota and metabolites in septic rats, which may be an effective therapy for patients with sepsis-related neurodamage.

Risks of overweight in the offspring of women with gestational diabetes at different developmental stages: A meta-analysis with more than half a million offspring.

We conducted a meta-analysis to evaluate the association of maternal gestational diabetes mellitus (GDM) and offspring overweight from birth to adulthood, and to assess the effects of lifestyle interventions in women with GDM on this risk of offspring overweight. We identified literature from PubMed and 12 other electronic databases and retrieved relevant literature published before October 20, 2020. Random-effects model analysis was used to calculate relative risks (RRs) of overweight and weighted mean differences of body mass index among children stratified into different developmental stages. Forty-nine cohort studies (n = 559,377) and four randomized controlled trials (n = 1277) were included. We found that offspring of women with GDM were at an increased risk for overweight with age, from 1.14 (95% confidence interval [CI]: 1.06-1.22) under 5 years, 1.37 (95% CI: 1.31-1.44) at 5 to <10 years, 2.00 (95% CI: 1.79-2.23) at 10 to <18 years, to 2.05 (95% CI: 1.65-2.55) over 18 years of age (p < 0.05 for differences among groups). However, it was not observed that lifestyle interventions for GDM decreased the elevated overweight risk (RR: 0.94, 95% CI: 0.80-1.11, I2 = 0.0%). These findings highlight the need for adopting an active and healthy lifestyle in this high-risk group.

Exacerbation of disease by intranasal liquid administration following influenza virus infection in mice.

Although numerous studies have clarified the synergistic pathogenesis in mouse models of influenza A virus (IAV)-associated dual infections, fewer studies have investigated the influence of intranasal liquid administration on the disease. This study explored the effects of intranasal PBS administration in mouse models of mimic IAV dual infection and the infectious dose of IAV that caused equivalent pathogenesis in different dual infection models. Weights, survival rates, virus loads, lung indexes and lung pathology were compared. We demonstrated that intranasal PBS administration following H1N1 or H3N2 infection increased weight loss, mortality, virus replication and lung damage. No difference was observed if the order was reversed or PBS was given simultaneously with IAV. To induce equivalent virulence, a 20-fold difference in the infectious dose was needed when the H3N2-PBS superinfection and H3N2-PBS coinfection or PBS-H3N2 superinfection groups were compared. Our study demonstrated that the unfavourable effect of intranasal liquid administration should not be neglected and that both the strain and infectious dose of IAV should be considered to avoid an illusion of synergistic pathogenicity when establishing IAV-associated dual infection model. A 20-fold lower dose than that of coinfection may be a better choice for secondary infection following IAV.

Status of glycosylated hemoglobin and prediction of glycemic control among patients with insulin-treated type 2 diabetes in North China: a multicenter observational study.

BACKGROUND: Blood glucose control is closely related to type 2 diabetes mellitus (T2DM) prognosis. This multicenter study aimed to investigate blood glucose control among patients with insulin-treated T2DM in North China and explore the application value of combining an elastic network (EN) with a machine-learning algorithm to predict glycemic control. METHODS: Basic information, biochemical indices, and diabetes-related data were collected via questionnaire from 2787 consecutive participants recruited from 27 centers in six cities between January 2016 and December 2017. An EN regression was used to address variable collinearity. Then, three common machine learning algorithms (random forest [RF], support vector machine [SVM], and back propagation artificial neural network [BP-ANN]) were used to simulate and predict blood glucose status. Additionally, a stepwise logistic regression was performed to compare the machine learning models. RESULTS: The well-controlled blood glucose rate was 45.82% in North China. The multivariable analysis found that hypertension history, atherosclerotic cardiovascular disease history, exercise, and total cholesterol were protective factors in glycosylated hemoglobin (HbA1c) control, while central adiposity, family history, T2DM duration, complications, insulin dose, blood pressure, and hypertension were risk factors for elevated HbA1c. Before the dimensional reduction in the EN, the areas under the curve of RF, SVM, and BP were 0.73, 0.61, and 0.70, respectively, while these figures increased to 0.75, 0.72, and 0.72, respectively, after dimensional reduction. Moreover, the EN and machine learning models had higher sensitivity and accuracy than the logistic regression models (the sensitivity and accuracy of logistic were 0.52 and 0.56; RF: 0.79, 0.70; SVM: 0.84, 0.73; BP-ANN: 0.78, 0.73, respectively). CONCLUSIONS: More than half of T2DM patients in North China had poor glycemic control and were at a higher risk of developing diabetic complications. The EN and machine learning algorithms are alternative choices, in addition to the traditional logistic model, for building predictive models of blood glucose control in patients with T2DM.

Regional Differences in the Prevalence of Coronary Heart Disease and Stroke in Patients With Type 2 Diabetes in China.

Background: There are large regional variations in the prevalence and mortality of cardiovascular disease in general populations in China. It remains uncertain whether the prevalence in type 2 diabetes mellitus (T2DM) varies by region in China. Methods: We analyzed data of 219,522 Chinese patients with T2DM retrieved from the China National HbA1c Surveillance System in 2012. We used the Chinese population distribution in 2010 to standardize prevalence of coronary heart disease (CHD), stroke, and composite of both in 30 provinces and seven geological regions. Multivariable logistic regression was performed to obtain ORs and CIs of provinces/geological regions for CHD, stroke, and composite of both. Results: Age and sex standardized prevalence of CHD, stroke, and composite of both was, respectively, 4.59% (95% CI, 4.58 to 4.60), 1.79% (1.79 to 1.80), and 5.85% (5.84 to 5.86), in contrast to 0.60% of CHD, 0.80% of stroke, and 1.37% of composite of both in the general population in China. After adjustment for traditional risk factors, Northeast had the highest risks of CHD, stroke, and composite of both, and North had the second highest risks of CHD, stroke, and composite of both among the seven regions, both being higher than any other regions (all P values < 0.05). The ORs of Northeast vs Southwest were up to 2.60 (2.35 to 2.88) for CHD, 2.49 (2.15 to 2.88) for stroke, and 2.61 (2.38 to 2.86) for composite of both. Conclusions: There were large variations in risks of CHD, stroke, and composite of both in T2DM in China with Northeast and North having the highest risks.