Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis.

BACKGROUND: It is uncertain how COVID-19 outbreak influences the hepatitis B epidemics. This study aims to evaluate the effects on hepatitis B owing to the COVID-19 outbreak and forecast the hepatitis B epidemiological trend in mainland China to speed up the course of the "End viral hepatitis Strategy". METHODS: We estimated the causal impacts and created a forecast through adopting monthly notifications of hepatitis B each year from 2005 to 2020 in mainland China using the Bayesian structural time series (BSTS) method. RESULTS: The hepatitis B epidemics fluctuates irregularly during the period 2005-2007(APC = 8.7, P = 0.246) and 2015-2020(APC = 1.7, P = 0.290), and there is a downturn (APC=-3.2, 95% CI -5.2 to -1.2, P = 0.006) from 2007 to 2015 in mainland China. The COVID-19 outbreak was found to have a monthly average reduction on the hepatitis B epidemics of 26% (95% CI 18-35%) within the first three months in 2020,17% (95% CI 7.7-26%) within the first six months in 2020, and 10% (95% CI19-22%) all year as a result of the COVID-19 outbreak, (probability of causal effect = 96.591%, P = 0.034) and the forecasts showed an upward trend from 2021 to 2025 (annual percentage change = 4.18, 95% CI 4.0 to 4.3, P < 0.001). CONCLUSION: The COVID-19 has a positive effect on the decline of hepatitis B cases. And the potential of BSTS model to forecast the epidemiological trend of the hepatitis B can be applied in automatic public health policymaking in mainland China.

A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA.

This prevalence of coronavirus disease 2019 (COVID-19) has become one of the most serious public health crises. Tree-based machine learning methods, with the advantages of high efficiency, and strong interpretability, have been widely used in predicting diseases. A data-driven interpretable ensemble framework based on tree models was designed to forecast daily new cases of COVID-19 in the USA and to determine the important factors related to COVID-19. Based on a hyperparametric optimization technique, we developed three machine learning algorithms based on decision trees, including random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), and three linear ensemble models were used to integrate these outcomes for better prediction accuracy. Finally, the SHapley Additive explanation (SHAP) value was used to obtain the feature importance ranking. Our outcomes demonstrated that, among the three basic machine learners, the prediction accuracy was the following in descending order: LightGBM, XGBoost, and RF. The optimized LAD ensemble was the most precise prediction model that reduced the prediction error of the best base learner (LightGBM) by approximately 3.111%, while vaccination, wearing masks, less mobility, and government interventions had positive effects on the control and prevention of COVID-19.

Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators. METHODS: We collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model. RESULTS: There were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) × (1, 1, 0)12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model. CONCLUSIONS: The multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.

Human Umbilical Cord Wharton's Jelly Derived Mesenchymal Stromal Cells May Attenuate Sarcopenia in Aged Mice Induced by Hindlimb Suspension.

BACKGROUND Since the use of human umbilical cord Wharton's Jelly derived mesenchymal stromal cells (hWJ-MSCs) to treat sarcopenia has not been explored, we studied the effects of hWJ-MSCs in aged male C57BL/6J mice with sarcopenia induced by hindlimb suspension, and explored the potential mechanism. MATERIAL AND METHODS Hindlimb suspension was used to induce sarcopenia in 24-month-old C57BL/6J mice and green fluorescent protein-tagged hWJ-MSCs and controls were transplanted into mice via tail vein or local intramuscular injection. After hWJ-MSC transplantation, changes in whole body muscle strength and endurance, gastrocnemius muscle weight and myofiber cross-sectional area (CSA) were studied. Proliferation of skeletal muscle stem cell, apoptosis, and chronic inflammation were also investigated. RESULTS We demonstrated that whole body muscle strength and endurance, gastrocnemius muscle mass, and CSA were significantly increased in hWJ-MSC-transplanted mice than in controls (P<0.05). In hWJ-MSC-transplanted mice, apoptotic myonuclei was reduced, and BrdU and Pax-7 expression indices of gastrocnemius muscles were increased (P<0.05). Tumor necrosis factor (TNF)-α and interleukin (IL)-6 were downregulated, and IL-4 and IL-10 were upregulated (P<0.05). CONCLUSIONS hWJ-MSCs may ameliorate sarcopenia in aged male C57BL/6J mice induced by hindlimb suspension, and this may be via activation of resident skeletal muscle satellite cells, reduction of apoptosis, and less chronic inflammation.

Effect of erythropoietin combined with hypothermia on serum tau protein levels and neurodevelopmental outcome in neonates with hypoxic-ischemic encephalopathy.

Although hypothermia therapy is effective to treat neonatal hypoxic-ischemic encephalopathy, many neonatal patients die or suffer from severe neurological dysfunction. Erythropoietin is considered one of the most promising neuroprotective agents. We hypothesized that erythropoietin combined with hypothermia will improve efficacy of neonatal hypoxic-ischemic encephalopathy treatment. In this study, 41 neonates with moderate/severe hypoxic-ischemic encephalopathy were randomly divided into a control group (hypothermia alone for 72 hours, n = 20) and erythropoietin group (hypothermia + erythropoietin 200 IU/kg for 10 days, n = 21). Our results show that compared with the control group, serum tau protein levels were lower and neonatal behavioral neurological assessment scores higher in the erythropoietin group at 8 and 12 days. However, neurodevelopmental outcome was similar between the two groups at 9 months of age. These findings suggest that erythropoietin combined with hypothermia reduces serum tau protein levels and improves neonatal behavioral neurology outcome but does not affect long-term neurodevelopmental outcome.