This study aimed to investigate the prevalence of HIV late presentation and advanced HIV disease and to identify the factors associated with HIV late presentation and advanced HIV disease among patients with newly diagnosed HIV/AIDS in the Guangxi Zhuang Autonomous Region, in Southwestern China. Patients with newly diagnosed HIV registered in the HIV surveillance system of Guangxi Centers for Disease Control between January 2012 and December 2016 were included in this study. Of 45,118 newly diagnosed patients, 70.2% had late presentation, and 45.1% had advanced HIV disease. A higher prevalence of late presentation and advanced HIV disease was found in male heterosexuals and female people who use drugs (PWID). Heterosexuals (OR 2.11 [95% CI 1.90–2.34]) and PWID (OR 1.55 [95% CI 1.30–1.84]) had a higher risk of late presentation than men who have sex with men (MSM). Blood testing of the blood receivers (OR 1.75 [95% CI 1.36–2.26]) and diagnosed in hospital (OR 1.74 [95% CI 1.65–1.84]) had an increased risk of late presentation compared to those who diagnosis in voluntary counseling and testing (VCT). Heterosexuals (OR 2.86 [95% CI 2.51–3.27]), PWID (OR 2.23 [95% CI 1.83–2.71]), blood testing of the blood receivers (OR 1.58 [95% CI 1.29–1.94]) and diagnosed in hospital (OR 1.85 [95% CI 1.76–1.94]) were also independent risk factors associated with advanced HIV disease. Older age, lower level of education and being divorced or widowed were also associated with late presentation and advanced HIV disease. Late presentation and advanced HIV disease were very common among patients with newly diagnosed HIV in Guangxi, China during 2012–2016. Targeted programs are urgently required to reduce HIV late diagnosis in Guangxi, especially for male heterosexuals, PWID, and patients with characteristics such as older age, lower level of education, divorced or widowed.

Background: New evidence implies that the imbalance of gut microbiota is associated with the progression of alcoholic liver disease (ALD) and that the composition of gut microbiota is altered in ALD patients. However, the predominant bacterium in patients involved in the progress of ALD has not been identified. The purpose of this study is to investigate the predominant bacterium in the early and end-stages of ALD as well as the relationship between the bacterium and the degree of liver injury.Methods: We enrolled 21 alcoholic fatty liver (AFL) patients, 17 alcoholic liver cirrhosis (ALC) patients and 27 healthy controls, and sequenced the 16S rRNA gene of their fecal microbiota. The gut microbiota composition and its relationship with the indicators of clinical hepatic function were assessed using canonical correspondence analysis (CCA), spearman correlation heatmap and multivariate association with linear (MaAsLin) Models.Results: The composition and structure of gut microbiota changed greatly in different stages of ALD, and the degree of disorder was aggravated with the progression of ALD, even in the early stage. Moreover, the relative abundance of Streptococcus was highly enriched only in patients with ALC (P <0.001), and positively correlated with AST level (P = 0.029). The abundance of Streptococcus distinguished the liver injury of ALC patients from the controls with an area under the receiver-operating characteristic curve (AUC) of 0.877 (P < 0.001).Conclusions: These findings indicate that the imbalance of gut microbiota exists at the early and end-stages of ALD, and the degree of disorder is aggravated with the progression of ALD. Streptococcus, as the predominant bacterium, may be a microbiological marker to evaluate the severity of liver injury in ALD patients.

Hepatitis 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. The 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. The 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. The 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.

Background: Many studies have explored changes in the gut microbiome associated with HIV infection, but the consistent pattern of changes has not been clarified. Men who have sex with men (MSM) are very likely to be an independent influencing factor of the gut microbiome, but relevant research is still lacking. Methods: We conducted a meta-analysis by screening 12 published studies of 16S rRNA gene amplicon sequencing of gut microbiomes related to HIV/AIDS (six of these studies contain data that is relevant and available to MSM) from NCBI and EBI databases. The analysis of gut microbiomes related to HIV infection status and MSM status included 1,288 samples (HIV-positive (HIV+) individuals, n = 744; HIV-negative (HIV–) individuals, n = 544) and 632 samples (MSM, n = 328; non-MSM, n = 304), respectively. The alpha diversity indexes, beta diversity indexes, differentially enriched genera, differentially enriched species, and differentially enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) functional pathways related to gut microbiomes were calculated. Finally, the overall trend of the above indicators was evaluated. Results: Our results indicate that HIV+ status is associated with decreased alpha diversity of the gut microbiome. MSM status is an important factor that affects the study of HIV-related gut microbiomes; that is, MSM are associated with alpha diversity changes in the gut microbiome regardless of HIV infection, and the changes in the gut microbiome composition of MSM are more significant than those of HIV+ individuals. A consistent change in Bacteroides caccae, Bacteroides ovatus, Bacteroides uniformis, and Prevotella stercorea was found in HIV+ individuals and MSM. The differential expression of the gut microbiome may be accompanied by changes in functional pathways of carbohydrate metabolism, amino acid metabolism, and lipid Metabolism. Conclusions: This study shows that the changes in the gut microbiome are related to HIV and MSM status. Importantly, MSM status may have a far greater impact on the gut microbiome than HIV status.

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)₁₂and 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.