Search

Abstract Objective Tuberculosis (TB) remains an important public health concern in western China. This study aimed to explore and analyze the spatial and temporal distribution characteristics of TB reported incidence in 12 provinces and municipalities in western China and to construct the optimal models for prediction, which would provide a reference for the prevention and control of TB and the optimization of related health policies. Methods We collected monthly data on TB reported incidence in 12 provinces and municipalities in western China and used ArcGIS software to analyze the spatial and temporal distribution characteristics of TB reported incidence. We applied the seasonal index method for the seasonal analysis of TB reported incidence and then established the SARIMA and Holt-Winters models for TB reported incidence in 12 provinces and municipalities in western China. Results The reported incidence of TB in 12 provinces and municipalities in western China showed apparent spatial clustering characteristics, and Moran’s I was greater than 0 (p 0.05). Conclusions As a hotspot of reported TB incidence, Tibet should continue to strengthen government leadership and policy support, explore TB intervention strategies and causes. The optimal prediction models we developed for reported TB incidence in 12 provinces and municipalities in western China were different.

Abstract Background This study aimed to construct a more accurate model to forecast the incidence of hand, foot, and mouth disease (HFMD) in mainland China from January 2008 to December 2019 and to provide a reference for the surveillance and early warning of HFMD. Methods We collected data on the incidence of HFMD in mainland China between January 2008 and December 2019. The SARIMA, SARIMA-BPNN, and SARIMA-PSO-BPNN hybrid models were used to predict the incidence of HFMD. The prediction performance was compared using the mean absolute error(MAE), mean squared error(MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation analysis. Results The incidence of HFMD in mainland China from January 2008 to December 2019 showed fluctuating downward trends with clear seasonality and periodicity. The optimal SARIMA model was SARIMA(1,0,1)(2,1,2)[12], with Akaike information criterion (AIC) and Bayesian Schwarz information criterion (BIC) values of this model were 638.72, 661.02, respectively. The optimal SARIMA-BPNN hybrid model was a 3-layer BPNN neural network with nodes of 1, 10, and 1 in the input, hidden, and output layers, and the R-squared, MAE, and RMSE values were 0.78, 3.30, and 4.15, respectively. For the optimal SARIMA-PSO-BPNN hybrid model, the number of particles is 10, the acceleration coefficients c1 and c2 are both 1, the inertia weight is 1, the probability of change is 0.95, and the values of R-squared, MAE, and RMSE are 0.86, 2.89, and 3.57, respectively. Conclusions Compared with the SARIMA and SARIMA-BPNN hybrid models, the SARIMA-PSO-BPNN model can effectively forecast the change in observed HFMD incidence, which can serve as a reference for the prevention and control of HFMD.

Abstract Background Human brucellosis is a serious public health concern in China. The objective of this study is to develop a suitable model for forecasting human brucellosis cases in mainland China. Methods Data on monthly human brucellosis cases from January 2012 to December 2021 in 31 provinces and municipalities in mainland China were obtained from the National Health Commission of the People’s Republic of China website. The TBATS and ELM models were constructed. The MAE, MSE, MAPE, and RMSE were calculated to evaluate the prediction performance of the two models. Results The optimal TBATS model was TBATS (1, {0,0}, -, {}) and the lowest AIC value was 1854.703. In the optimal TBATS model, {0,0} represents the ARIMA (0,0) model, {} are the parameters of the seasonal periods and the corresponding number of Fourier terms, respectively, and the parameters of the Box-Cox transformation ω are 1. The optimal ELM model hidden layer number was 33 and the R-squared value was 0.89. The ELM model provided lower values of MAE, MSE, MAPE, and RMSE for both the fitting and forecasting performance. Conclusions The results suggest that the forecasting performance of ELM model outperforms the TBATS model in predicting human brucellosis between January 2012 and December 2021 in mainland China. Forecasts of the ELM model can help provide early warnings and more effective prevention and control measures for human brucellosis in mainland China.

Abstract Globally, since the outbreak of the Omicron variant in November 2021, the number of confirmed cases of COVID-19 has continued to increase, posing a tremendous challenge to the prevention and control of this infectious disease in many countries. The global daily confirmed cases of COVID-19 between November 1, 2021, and February 17, 2022, were used as a database for modeling, and the ARIMA, MLR, and Prophet models were developed and compared. The prediction performance was evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The study showed that ARIMA (7, 1, 0) was the optimum model, and the MAE, MAPE, and RMSE values were lower than those of the MLR and Prophet models in terms of fitting performance and forecasting performance. The ARIMA model had superior prediction performance compared to the MLR and Prophet models. In real-world research, an appropriate prediction model should be selected based on the characteristics of the data and the sample size, which is essential for obtaining more accurate predictions of infectious disease incidence.

Summary: Azoospermia is a significant cause of male infertility, with non-obstructive azoospermia (NOA) being the most severe type of spermatogenic failure. NOA is mostly caused by congenital factors, but our understanding of its genetic causes is very limited. Here, we identified a frameshift variant (c.201_202insAC, p.Tyr68Thrfs∗17) and two nonsense variants (c.1897C>T, p.Gln633∗; c.2005C>T, p.Gln669∗) in KCTD19 (potassium channel tetramerization domain containing 19) from two unrelated infertile Chinese men and a consanguineous Pakistani family with three infertile brothers. Testicular histological analyses revealed meiotic metaphase I (MMI) arrest in the affected individuals. Mice modeling KCTD19 variants recapitulated the same MMI arrest phenotype due to severe disrupted individualization of MMI chromosomes. Further analysis showed a complete loss of KCTD19 protein in both Kctd19 mutant mouse testes and affected individual testes. Collectively, our findings demonstrate the pathogenicity of the identified KCTD19 variants and highlight an essential role of KCTD19 in MMI chromosome individualization.

Exploring the genetic basis of human infertility is currently under intensive investigation. However, only a handful of genes have been validated in animal models as disease-causing genes in infertile men. Thus, to better understand the genetic basis of human spermatogenesis and bridge the knowledge gap between humans and other animal species, we construct the FertilityOnline, a database integrating the literature-curated functional genes during spermatogenesis into an existing spermatogenic database, SpermatogenesisOnline 1.0. Additional features, including the functional annotation and genetic variants of human genes, are also incorporated into FertilityOnline. By searching this database, users can browse the functional genes involved in spermatogenesis and instantly narrow down the number of candidates of genetic mutations underlying male infertility in a user-friendly web interface. Clinical application of this database was exampled by the identification of novel causative mutations in synaptonemal complex central element protein 1 (SYCE1) and stromal antigen 3 (STAG3) in azoospermic men. In conclusion, FertilityOnline is not only an integrated resource for spermatogenic genes but also a useful tool facilitating the exploration of the genetic basis of male infertility. FertilityOnline can be freely accessed at http://mcg.ustc.edu.cn/bsc/spermgenes2.0/index.html.

Multiple morphological abnormalities of the sperm flagella (MMAF) are the most severe form of asthenozoospermia due to impaired axoneme structure in sperm flagella. Dynein arms are necessary components of the sperm flagellar axoneme. In this study, we recruited 3 unrelated consanguineous Pakistani families with multiple MMAF-affected individuals, who had no overt ciliary symptoms. Whole-exome sequencing and Sanger sequencing identified 2 cilia and flagella associated protein 57 (CFAP57) loss-of-function mutations (c.2872C>T, p. R958*; and c.2737C>T, p. R913*) recessively segregating with male infertility. A mouse model mimicking the mutation (c.2872C>T) was generated and recapitulated the typical MMAF phenotype of CFAP57-mutated individuals. Both CFAP57 mutations caused loss of the long transcript-encoded CFAP57 protein in spermatozoa from MMAF-affected individuals or from the Cfap57-mutant mouse model while the short transcript was not affected. Subsequent examinations of the spermatozoa from Cfap57-mutant mice revealed that CFAP57 deficiency disrupted the inner dynein arm (IDA) assembly in sperm flagella and that single-headed IDAs were more likely to be affected. Thus, our study identified 2 pathogenic mutations in CFAP57 in MMAF-affected individuals and reported a conserved and pivotal role for the long transcript-encoded CFAP57 in IDAs’ assembly and male fertility.

Abstract. Hepatitis B virus infection is a major global public health concern. This study explored the epidemic characteristics and tendency of hepatitis B in 31 provinces of mainland China, constructed a SARIMA model for prediction, and provided corresponding preventive measures. Monthly hepatitis B case data from mainland China from 2013 to 2020 were obtained from the website of the National Health Commission of the People's Republic of China. Monthly data from 2013 to 2020 were used to build the SARIMA model and data from 2021 were used to test the model. Between 2013 and 2020, 9,177,313 hepatitis B cases were reported in mainland China. SARIMA(1,0,0)(0,1,1)12 was the optimal model and its residual was white noise. It was used to predict the number of hepatitis B cases from January to December 2021, and the predicted values for 2021 were within the 95% confidence interval. This study suggests that the SARIMA model simulated well based on epidemiological trends of hepatitis B in mainland China. The SARIMA model is a feasible tool for monitoring hepatitis B virus infections in mainland China.

Multiple morphological abnormalities of the flagella (MMAF) can lead to male infertility due to impaired sperm motility and morphology. Calmodulin- and spoke-associated complex (CSC) are known for their roles in radial spoke (RS) assembly and ciliary motility in Chlamydomonas, while the role of cilia- and flagella-associated protein 61 (CFAP61), a mammalian ortholog of the CSC subunits, in humans is yet unknown. Here, we recruited three unrelated Pakistani families comprising of 11 infertile male patients diagnosed with MMAF. CFAP61 variants, c.451_452del (p.I151Nfs*4) in family 1 and c.847C > T (p.R283*) in family 2 and 3, were identified recessively co-segregating with the MMAF phenotype. Transmission electron microscopy analyses revealed severe disorganized axonemal ultrastructures, and missings of central pair, RSs, and inner dynein arms were also observed and confirmed by immunofluorescence staining in spermatozoa from patients. CFAP61 and CFAP251 signals were absent from sperm tails of the patients, which suggested the loss of functional CSC in sperm flagella. Altogether, our findings report that homozygous variants in CFAP61 are associated with MMAF and male infertility, demonstrating the essential role of this gene in normal sperm flagellum structure in humans.

Background and objective Tuberculosis (Tuberculosis, TB) is a public health problem in China, which not only endangers the population’s health but also affects economic and social development. It requires an accurate prediction analysis to help to make policymakers with early warning and provide effective precautionary measures. In this study, ARIMA, GM(1,1), and LSTM models were constructed and compared, respectively. The results showed that the LSTM was the optimal model, which can be achieved satisfactory performance for TB cases predictions in mainland China. Methods The data of tuberculosis cases in mainland China were extracted from the National Health Commission of the People’s Republic of China website. According to the TB data characteristics and the sample requirements, we created the ARIMA, GM(1,1), and LSTM models, which can make predictions for the prevalence trend of TB. The mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were applied to evaluate the effects of model fitting predicting accuracy. Results There were 3,021,995 tuberculosis cases in mainland China from January 2018 to December 2020. And the overall TB cases in mainland China take on a downtrend trend. We established ARIMA, GM(1,1), and LSTM models, respectively. The optimal ARIMA model is the ARIMA (0,1,0) × (0,1,0)12. The equation for GM(1,1) model was X(k+1) = -10057053.55e(-0.01k) + 10153178.55 the Mean square deviation ratio C value was 0.49, and the Small probability of error P was 0.94. LSTM model consists of an input layer, a hidden layer and an output layer, the parameters of epochs, learning rating are 60, 0.01, respectively. The MAE, RMSE, and MAPE values of LSTM model were smaller than that of GM(1,1) and ARIMA models. Conclusions Our findings showed that the LSTM model was the optimal model, which has a higher accuracy performance than that of ARIMA and GM (1,1) models. Its prediction results can act as a predictive tool for TB prevention measures in mainland China.

Male infertility is a prevalent disorder distressing an estimated 70 million people worldwide. Despite continued progress in understanding the causes of male infertility, idiopathic sperm abnormalities such as multiple morphological abnormalities of sperm flagella (MMAF) still account for about 30% of male infertility. Recurrent mutations in DNAH1 have been reported to cause MMAF in various populations, but the underlying mechanism is still poorly explored. This study investigated the MMAF phenotype of two extended consanguineous Pakistani families without manifesting primary ciliary dyskinesia symptoms. The transmission electron microscopy analysis of cross-sections of microtubule doublets revealed a missing central singlet of microtubules and a disorganized fibrous sheath. SPAG6 staining, a marker generally used to check the integration of microtubules of central pair, further confirmed the disruption of central pair in the spermatozoa of patients. Thus, whole-exome sequencing (WES) was performed, and WES analysis identified two novel mutations in the DNAH1 gene that were recessively co-segregating with MMAF phenotype in both families. To mechanistically study the impact of identified mutation, we generated Dnah1 mice models to confirm the in vivo effects of identified mutations. Though Dnah1△iso1/△iso1 mutant mice represented MMAF phenotype, no significant defects were observed in the ultrastructure of mutant mice spermatozoa. Interestingly, we found DNAH1 isoform2 in Dnah1△iso1/△iso1 mutant mice that may be mediating the formation of normal ultrastructure in the absence of full-length protein. Altogether we are first reporting the possible explanation of inconsistency between mouse and human DNAH1 mutant phenotypes, which will pave the way for further understanding of the underlying pathophysiological mechanism of MMAF.

Meiosis, an essential step in gametogenesis, is the key event in sexually reproducing organisms. Thousands of genes have been reported to be involved in meiosis. Therefore, a specialist database is much needed for scientists to know about the function of these genes quickly and to search for genes with potential roles in meiosis. Here, we developed “MeiosisOnline,” a publicly accessible, comprehensive database of known functional genes and potential candidates in meiosis (https://mcg.ustc.edu.cn/bsc/meiosis/index.html). A total of 2,052 meiotic genes were manually curated from literature resource and were classified into different categories. Annotation information was provided for both meiotic genes and predicted candidates, including basic information, function, protein–protein interaction (PPI), and expression data. On the other hand, 165 mouse genes were predicted as potential candidates in meiosis using the “Greed AUC Stepwise” algorithm. Thus, MeiosisOnline provides the most updated and detailed information of experimental verified and predicted genes in meiosis. Furthermore, the searching tools and friendly interface of MeiosisOnline will greatly help researchers in studying meiosis in an easy and efficient way.

Introduction: Seasonal influenza is a serious public health issue in China. This study aimed to develop a new hybrid model for seasonal influenza incidence prediction and provide reference information for early warning management before outbreaks. Methodology: Data on the monthly incidence of seasonal influenza between 2004 and 2018 were obtained from the China Public Health Science Data Center website. A single seasonal autoregressive integrated moving average (SARIMA) model and a single error trend and seasonality (ETS) model were built. On this basis, we constructed SARIMA, ETS, and support vector regression (SARIMA-ETS-SVR) hybrid model. The prediction performance was determined by comparing mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE) indices. Results: The optimum SARIMA model was SARIMA (0,1,0) (0,0,1)12. Error trend and seasonality (ETS) (M,A,M) was the SARIMA optimal model. For the fitting performance, the SARIMA-ETS-SVR hybrid model achieved the lowest values of MAE, MSE, and RMSE, in addition to the MAPE. In terms of predictive performance, the SARIMA-ETS-SVR hybrid model had the lowest MAE, MSE, MAPE, and RMSE values among the three models. Conclusions: The study demonstrated that the SARIMA-ETS-SVR hybrid model provides better generalization ability than a single SARIMA model and a single ETS model, and the predictions will provide a useful tool for preventing this infectious disease. [ABSTRACT FROM AUTHOR]