Meteorological and social conditions contribute to infectious diarrhea in China

Berberine protects against dysentery by targeting both Shigella filamentous temperature sensitive protein Z and host pyroptosis: Resolving in vitro-vivo effect discrepancy

BACKGROUND

Berberine (BBR), an isoquinoline alkaloid, has been applied clinically to treat dysentery caused by Shigella for decades. Nevertheless, the precise mechanisms behind its anti-Shigella effect have not been fully elucidated.

PURPOSE

This study aims to investigate the mechanism of BBR on antibacterial activity against S. flexneri infection.

METHODS

We initially reproduced the mouse model of Shigella flexneri-induced dysentery, and then, assessed the therapeutic effect of BBR. In vitro, we measured the inhibitory effect of BBR against S. flexneri and the GTPase activity of FtsZ (filamentous temperature sensitive protein Z) using the minimum inhibitory concentration (MIC) test and an enzyme activity assay to investigate the bacteria-directed mechanisms. Subsequently, we utilized both the in vivo mouse model of dysentery and the in vitro macrophage infection model with S. flexneri to explore the host-directed anti-Shigella mechanisms of BBR. The canonical pyroptosis pathway mediated by caspase-1 and mitochondrial damage were examined by Western blot, immunofluorescence and RNA interference analysis.

RESULTS

Administration of BBR alleviated the symptoms of dysentery induced by S. flexneri infection. In vitro, BBR could inhibit the growth of S. flexneri by targeting the GTPase activity of FtsZ, thereby affecting bacterial cell division. Additionally, our in vivo findings revealed that BBR suppressed macrophage pyroptosis by inhibiting the expression of caspase-1 and subsequently the mitochondrial damage, which in turn reduced the intestinal inflammation and tissue damage.

CONCLUSIONS

Our results provide a novel mechanism of BBR's action, which targets both the bacterium and the host to exert its antibacterial effects. Furthermore, it also provides an explanation for the discrepancy between BBR's relatively modest antibacterial efficacy in vitro and its enhanced antibacterial effects in vivo, thus, giving support to its clinical use.

Predicting the incidence of infectious diarrhea with symptom surveillance data using a stacking-based ensembled model

BACKGROUND

Infectious diarrhea remains a major public health problem worldwide. This study used stacking ensemble to developed a predictive model for the incidence of infectious diarrhea, aiming to achieve better prediction performance.

METHODS

Based on the surveillance data of infectious diarrhea cases, relevant symptoms and meteorological factors of Guangzhou from 2016 to 2021, we developed four base prediction models using artificial neural networks (ANN), Long Short-Term Memory networks (LSTM), support vector regression (SVR) and extreme gradient boosting regression trees (XGBoost), which were then ensembled using stacking to obtain the final prediction model. All the models were evaluated with three metrics: mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE).

RESULTS

Base models that incorporated symptom surveillance data and weekly number of infectious diarrhea cases were able to achieve lower RMSEs, MAEs, and MAPEs than models that added meteorological data and weekly number of infectious diarrhea cases. The LSTM had the best prediction performance among the four base models, and its RMSE, MAE, and MAPE were: 84.85, 57.50 and 15.92%, respectively. The stacking ensembled model outperformed the four base models, whose RMSE, MAE, and MAPE were 75.82, 55.93, and 15.70%, respectively.

CONCLUSIONS

The incorporation of symptom surveillance data could improve the predictive accuracy of infectious diarrhea prediction models, and symptom surveillance data was more effective than meteorological data in enhancing model performance. Using stacking to combine multiple prediction models were able to alleviate the difficulty in selecting the optimal model, and could obtain a model with better performance than base models.

Epidemic Characteristics, Spatiotemporal Pattern, and Risk Factors of Other Infectious Diarrhea in Fujian Province From 2005 to 2021: Retrospective Analysis

BACKGROUND

Other infectious diarrhea (OID) continues to pose a significant public health threat to all age groups in Fujian Province. There is a need for an in-depth analysis to understand the epidemiological pattern of OID and its associated risk factors in the region.

OBJECTIVE

In this study, we aimed to describe the overall epidemic characteristics and spatiotemporal pattern of OID in Fujian Province from 2005 to 2021 and explore the linkage between sociodemographic and environmental factors and the occurrence of OID within the study area.

METHODS

Notification data for OID in Fujian were extracted from the China Information System for Disease Control and Prevention. The spatiotemporal pattern of OID was analyzed using Moran index and Kulldorff scan statistics. The seasonality of and short-term impact of meteorological factors on OID were examined using an additive decomposition model and a generalized additive model. Geographical weighted regression and generalized linear mixed model were used to identify potential risk factors.

RESULTS

A total of 388,636 OID cases were recorded in Fujian Province from January 2005 to December 2021, with an average annual incidence of 60.3 (SD 16.7) per 100,000 population. Children aged <2 years accounted for 50.7% (196,905/388,636) of all cases. There was a steady increase in OID from 2005 to 2017 and a clear seasonal shift in OID cases from autumn to winter and spring between 2005 and 2020. Higher maximum temperature, atmospheric pressure, humidity, and precipitation were linked to a higher number of deseasonalized OID cases. The spatial and temporal aggregations were concentrated in Zhangzhou City and Xiamen City for 17 study years. Furthermore, the clustered areas exhibited a dynamic spreading trend, expanding from the southernmost Fujian to the southeast and then southward over time. Factors such as densely populated areas with a large <1-year-old population, less economically developed areas, and higher pollution levels contributed to OID cases in Fujian Province.

CONCLUSIONS

This study revealed a distinct distribution of OID incidence across different population groups, seasons, and regions in Fujian Province. Zhangzhou City and Xiamen City were identified as the major hot spots for OID. Therefore, prevention and control efforts should prioritize these specific hot spots and highly susceptible groups.

A microfluidic chip-based multiplex PCR-reverse dot blot hybridization technique for rapid detection of enteropathogenic bacteria

Diarrhea caused by enteropathogenic bacteria is a major public health issue worldwide, especially in developing countries. In this study, a microfluidic chip-based multiplex polymerase chain reaction (PCR)-reverse dot blot hybridization technology for the rapid and simultaneous detection of 11 enteropathogenic bacteria was developed and the entire process was completed within 3-4 h. The specificity of this method was analyzed using 11 types of pure target bacterial colonies and another 7 types of pure bacterial colonies, and its sensitivity was evaluated with the serial 10-fold dilution of 11 types of pure target bacterial colonies. The detection limit of this method was as low as 10³-10² CFU/mL, and it exhibited high specificity for enteropathogenic bacteria. A total of 60 clinical diarrheal fecal samples were detected using this method, the results of which were compared with those of the conventional reference method, which resulted in a positive coincident rate of 100% and a negative coincident rate of 93.75%. Based on the findings, it could be concluded that multiplex PCR-reverse dot blot hybridization based on the microfluidic chip is a rapid, economical, sensitive, specific, and high-throughput method for detecting enteropathogenic bacteria.

Temporal trend of diarrhea morbidity rate with climate change: Egypt as a case study

Many studies have detected a relationship between diarrhea morbidity rates with the changes in precipitation, temperature, floods, droughts, water shortage, etc. But, most of the authors were cautious in their studies, because of the lack of empirical climate-health data and there were large uncertainties in the future projections. The study aimed to refine the link between the morbidity rates of diarrhea in some Egyptian governorates representative of the three Egyptian geographical divisions with the meteorological changes that occurred in the 2006-2016 period for which the medical data are available, as a case study. Medical raw data was collected from the Information Centre Department of the Egyptian Ministry of Health and Population. The meteorological data of temperature and precipitation extremes were defined as data outside the 10th-90th percentile range of values of the period of study, and their analysis was done using a methodology similar to the one recommended by the WMO and integrated in the CLIMDEX software. Relationships between the morbidity rates of diarrhea in seven Egyptian governorates and the meteorological changes that occurred in the period 2006 to 2016 were analyzed using multiple linear regression analysis to identify the most effective meteorological factor that affects the trend of morbidity rate of diarrhea in each governorate. Statistical analysis revealed that some meteorological parameters can be used as predictors for morbidity rates of diarrhea in Cairo, Alexandria, and Gharbia, but not in Aswan, Behaira, and Dakahlia where the temporal evolution cannot be related with meteorology. In Red Sea, there was no temporal trend and no significant relationships between the diarrhea morbidity rate and meteorological parameters. The predictor meteorological parameters for morbidity rates of diarrhea were found to be depending on the geographic locations and infrastructures in these governorates. It was concluded that the meteorological data that can be used as predictors for the morbidity rate of diarrhea is depending on the geographical location and infrastructures of the target location. The socioeconomic levels as well as the infrastructures in the governorate must be considered confounders in future studies.

Meteorological Factors Affecting Infectious Diarrhea in Different Climate Zones of China

Meteorological factors and the increase in extreme weather events are closely related to the incidence rate of infectious diarrhea. However, few studies have explored whether the impact of the same meteorological factors on the incidence rate of infectious diarrhea in different climate regions has changed and quantified these changes. In this study, the time series fixed-effect Poisson regression model guided by climate was used to quantify the relationships between the incidence rate of various types of infectious diarrhea and meteorological factors in different climate regions of China from 2004 to 2018, with a lag of 0-2 months. In addition, six social factors, including per capita Gross Domestic Product (GDP), population density, number of doctors per 1000 people, proportion of urbanized population, proportion of children aged 0-14 years old, and proportion of elderly over 65 years old, were included in the model for confounding control. Additionally, the intercept of each province in each model was analyzed by a meta-analysis. Four climate regions were considered in this study: tropical monsoon areas, subtropical monsoon areas, temperate areas and alpine plateau areas. The results indicate that the influence of meteorological factors and extreme weather in different climate regions on diverse infectious diarrhea types is distinct. In general, temperature was positively correlated with all infectious diarrhea cases (0.2 ≤ r ≤ 0.6, p < 0.05). After extreme rainfall, the incidence rate of dysentery in alpine plateau area in one month would be reduced by 18.7% (95% confidence interval (CI): -27.8--9.6%). Two months after the period of extreme sunshine duration happened, the incidence of dysentery in the alpine plateau area would increase by 21.9% (95% CI: 15.4-28.4%) in that month, and the incidence rate of typhoid and paratyphoid in the temperate region would increase by 17.2% (95% CI: 15.5-18.9%) in that month. The meta-analysis showed that there is no consistency between different provinces in the same climate region. Our study indicated that meteorological factors and extreme weather in different climate areas had different effects on various types of infectious diarrhea, particularly extreme rainfall and extreme sunshine duration, which will help the government develop disease-specific and location-specific interventions, especially after the occurrence of extreme weather.