Talaromyces marneffei infection is commonly found in hospitalized HIV/AIDS patients in southern China and was associated with a higher mortality rate than most HIV-associated complications. These results highlight the need for improved diagnosis, treatment and prevention of infection by this neglected fungal pathogen in southern China.
BackgroundHepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease.MethodsThe autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models.ResultsThe morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models.ConclusionsThe hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County.
BackgroundTo examine the accuracy of Rapid Diagnosis of Talaromyces marneffei (RDTM) in order to improve diagnosis and treatment for clinical measures and reduce the mortality due to associated infections.MethodsIn this systematic review and meta-analysis, we screened PubMed, Ovid (Cochrane library) and Web of Science, Chinese database CNKI and Wanfang for articles published between 1956 and December, 2017. Data were taken from cross-sectional studies as well as from baseline measurements in longitudinal studies with clinical follow-up. Articles were excluded if they did not contain a cohort with T. marneffei and a control cohort or a cohort with standard fungus culture. Data were extracted by two authors and checked by three for accuracy. For quality assessment, modified QUADAS-2 criteria were used.ResultsThe 26 included diagnostic studies enrolled 5,594 objectives in 632 patients with T. marneffei infections and 2,612 negative controls between 1996 and 2017 in Thailand, Vietnam and China. The total combined sensitivity and specificity of rapid diagnosis of T. marneffei was 0.82 (95% CI: 0.68–0.90) and 0.99 (95% CI: 0.98–1.00). According to the experimental method, the included studies can be divided into three subgroups, including PCR-based, ELISA-based and others. The results showed these three subgroups had a highly pooled specificity of 1.00 (95% CI: 0.99–1.00), 0.99 (0.98–1.00) and 0.97 (95% CI: 0.91–1.00), respectively, while combined sensitivity was 0.84 (95% CI: 0.37–0.98), 0.82 (95% CI: 0.64–0.92) and 0.77 (95% CI: 0.54–0.91), respectively.ConclusionsAlthough serological methods with a high specificity is essential for potential rapid diagnostic, false-negative results can be obtained in the serum samples, there is no suitable rapid serological test to refer to as is the case with TM infection.
Guangxi, a province in southwestern China, has the second highest reported number of HIV/AIDS cases in China. This study aimed to develop an accurate and effective model to describe the tendency of HIV and to predict its incidence in Guangxi. HIV incidence data of Guangxi from 2005 to 2016 were obtained from the database of the Chinese Center for Disease Control and Prevention. Long short-term memory (LSTM) neural network models, autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models and exponential smoothing (ES) were used to fit the incidence data. Data from 2015 and 2016 were used to validate the most suitable models. The model performances were evaluated by evaluating metrics, including mean square error (MSE), root mean square error, mean absolute error and mean absolute percentage error. The LSTM model had the lowest MSE when the N value (time step) was 12. The most appropriate ARIMA models for incidence in 2015 and 2016 were ARIMA (1, 1, 2) (0, 1, 2)12 and ARIMA (2, 1, 0) (1, 1, 2)12, respectively. The accuracy of GRNN and ES models in forecasting HIV incidence in Guangxi was relatively poor. Four performance metrics of the LSTM model were all lower than the ARIMA, GRNN and ES models. The LSTM model was more effective than other time-series models and is important for the monitoring and control of local HIV epidemics.
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