Ning Zang

Application of a Combined Model with Autoregressive Integrated Moving Average (ARIMA) and Generalized Regression Neural Network (GRNN) in Forecasting Hepatitis Incidence in Heng County, China.

Background 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. Methods 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. Results 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. Conclusions 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.

Downregulation of autophagy-related gene ATG5 and GABARAP expression by IFN-λ1 contributes to its anti-HCV activity in human hepatoma cells

ABSTRACT Type‐III interferon (IFN‐&lgr;), the most recently discovered family of IFNs, shares common features with type I IFNs, but also has many distinctive activities. It is not clear that whether IFN‐&lgr; has additional antiviral mechanisms. In this study, we investigated the effects of IFN‐&lgr; on autophagy, a cellular process closely related to hepatitis C virus (HCV) infection in human hepatoma Huh7 cells. Our results showed that IFN‐&lgr;1 treatment inhibit autophagic activity in Huh7 cells, as evidenced by the decreased expression of microtubule‐associated protein 1 light chain 3B (LC3B)‐II and conversion of LC3B‐I to LC3B‐II, decreased formation of GFP‐LC3 puncta and accumulation of autophagosomes. IFN‐&lgr;1 could also inhibit HCV‐induced or tunicamycin (a known inducer of autophagy with similar mechanism to HCV infection) ‐induced LC3B‐II expression and autophagosome formation. Through PCR array, real time RT PCR, and western blot, two autophagy‐related genes, ATG5 and GABARAP, were identified and verified to be down‐regulated by IFN‐&lgr;1 treatment, either in HCV‐uninfected Huh7 cells or in HCV JFH‐1‐infected cells. Overexpression of ATG5 and/or GABARAP could partly recover the IFN‐&lgr;1‐inhibited HCV replication. Mechanism research demonstrated that IFN‐&lgr;1 could induce the expression of miR‐181a and miR‐214 (targeting ATG5 and GABARAP respectively), by which down‐regulates ATG5 and GABARAP expression. Taken together, our results indicate that suppression of the autophagy response by IFN‐&lgr;1 contributes to IFN‐&lgr;1 anti‐HCV activity. The results also provide a theoretical basis for improving the effectiveness of IFN treatment of HCV infection through inhibition of the HCV‐induced autophagy response. HIGHLIGHTSIFN‐&lgr;1 suppresses cellular autophagy in Huh7 cells, which contributes to IFN‐&lgr;1 anti‐HCV activity.IFN‐&lgr;1 induces the expression of miR‐181a and miR‐214, by which down‐regulates expression of ATG5 and GABARAP.Type I IFN (IFN‐&agr;2a) has little effect on autophagy in Huh7 cells.

Accuracy of rapid diagnosis of Talaromyces marneffei: A systematic review and meta-analysis

Background To 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. Methods In 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. Results The 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. Conclusions Although 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.

Alcohol attenuates anti-HCV function of IFN-λ1 through up-regulation of PLASy expression in human hepatic cells

Alcohol could compromise the anti‐hepatitis C virus (HCV) function of interferon‐alpha (IFN‐α). However, little information is available about the effect of alcohol on interferon‐lambda (IFN‐λ, type III IFN), a novel candidate for development of therapy for HCV infection. Huh7 cells were infected with HCV JFH‐1 virus, then treated with alcohol, and/or IFN‐λ1. RT‐PCR and Western blot were used to detect the levels of HCV and key cellular factors. Overexpression or silencing expression was performed to verify the role of key factors in alcohol‐attenuated anti‐HCV function of IFN‐λ1. Alcohol treatment compromised anti‐HCV effect of IFN‐λ1 in HCV JFH‐1‐infected Huh7 cells, evidenced by the significantly increased levels of HCV RNA, and HCV core protein in alcohol‐/IFN‐λ1‐treated cells compared to cells with IFN‐λ1 treatment alone. Investigation of the mechanisms responsible for the alcohol action revealed that alcohol enhanced the expression of protein inhibitor of activated STAT (PIASy). Overexpression of PIASy compromised anti‐HCV ability of IFN‐λ1, whereas silencing expression of PIASy partly restored the alcohol‐attenuated anti‐HCV effect of IFN‐λ1. More importantly, overexpression of PIASy significantly down‐regulated the level of IFN‐λ1‐indcued phosphorylation of STAT1 (p‐STAT1), an important adaptor in IFN‐λ pathway, as well as reduced the expression of IFN‐λ1‐induced IFN‐stimulated genes 56 (ISG56), and myxovirus resistance 1 (Mx1), two antiviral effectors in in IFN‐λ pathway. These findings indicate that alcohol, through inducing the expression of negative regulator in IFN‐λ pathway, inhibits IFN‐λ‐mediated anti‐HCV action in human hepatic cells, which may lead to the poor efficacy of IFN‐λ‐based therapy against HCV infection.

mircoRNA-3162-3p is a potential biomarker to identify new infections in HIV-1-infected patients

BACKGROUND Identification of new HIV infections (HIV incidence) is critical for monitoring AIDS epidemic and assessing the effectiveness of intervention measures. However, current methods for distinguishing new infections from newly diagnosed HIV-1 patients are still imperfect. We explored utilizing miRNAs as biomarker to identify HIV new infections. METHODS According to the HIV-1 status and the estimated duration of infection (EDI), we enrolled participants and divided them into three groups: healthy control, new infection (within 1 year), and old infection (longer than 1 year). Participants were assigned into screening set or validation set. miRNA microarray was performed in screening set and the differentially expressed miRNAs were screened out. The differentially expressed miRNAs were further confirmed in validation set and HIV-1 IIIB-MT2 cells infection system. RESULTS In screening set, 5 miRNAs including miR-1291, miR-3609, miR-3162-3p, miR-874-5p and miR-4258 were screened out for their differential expression in plasma among three groups. In validation set, down- trend of miR-3162-3p was validated from healthy control, new infection to old infection groups. In HIV-1 IIIB-MT2 system, the levels of miR-3162-3p also decreased along with infection duration in vitro. Sensitivity and specificity for miR-3162-3p to distinguish new infection from old infection were 100.0% and 71.43%, respectively, with the cut-off value of 0.916. CONCLUSION miR-3162-3p in plasma could be a potential microRNA biomarker to identify HIV new infections in HIV-1 infected patients.

Alcohol-induced autophagy via upregulation of PIASy promotes HCV replication in human hepatoma cells

Both alcohol and hepatitis C virus (HCV) infection could induce cellular autophagy in liver cells, which is considered to be essential for productive HCV replication. However, whether alcohol-induced autophagy is involved in the pathogenesis of HCV infection is still poorly understood. Alcohol treatment could induce autophagy in Huh7 cells (a hepatoma cell line that supports HCV JFH-1 replication), evidenced by the increase of LC3B-II levels, the conversion of LC3B-I to LC3B-II, and the formation of GFP-LC3 puncta as well as the decrease of p62 level in alcohol-treated cells compared with control cells. Alcohol treatment also significantly increased PIASy (a member of the PIAS family) expression, which can act as a SUMO (small ubiquitin-like modifier protein) E3 ligase to regulate a broader range of cellular processes including autophagy. Overexpression or the silencing expression of PIASy in alcohol-treated Huh7 cells could increase or decrease autophagic activation caused by alcohol treatment, respectively, and thus affect HCV replication correspondingly. In the absence of alcohol, overexpression or silencing expression of PIASy increase or decrease the level of cellular autophagy, judged by the changes of LC3B-II and p62 levels in the presence or absence of chloroquine (CQ), a lysosome inhibitor. More importantly, in the presence of 3-methyladenine (3-MA), an inhibitor in the early stage of autophagy, the effects of overexpression or silencing expression of PIASy on HCV replication were largely blocked. Furthermore, PIASy could selectively drive the accumulation of SUMO1-conjugated proteins, along with upregulation of the expression of several important autophagy factors, including ATG7 and ATG5–ATG12. In conclusion, alcohol promotes HCV replication through activation of autophagy in Huh7 cells, which partly attributes to its induction of PIASy expression. PIASy-enhanced accumulation of SUMO1-conjugated proteins may contribute to its inducing effect of autophagy. Our findings provide a novel mechanism for the action of alcohol-promoting HCV replication in the context of cellular autophagy.

Western Blot-Based Logistic Regression Model for the Identification of Recent HIV-1 Infection: A Promising HIV-1 Surveillance Approach for Resource-Limited Regions

Objectives Identifying recent infections is necessary to monitor HIV/AIDS epidemic; however, it needs to be further developed. Methods and Results Participants were defined as having recent infection or older infection according to the estimated duration of HIV-1 infection and further assigned into training set and validation set according to their entering time points. Western blot (WB) confirmatory test and BED-CEIA were performed. The performance of the two methods on recent HIV-1 diagnosis was evaluated and compared. 81 subjects were enrolled in the training set and 72 in the validation set. Relative grey ratios of p24, p39, p31, p66, gp41, and gp160 were significantly higher in older infected patients of the training set. The present status of p55 was more frequently missing in recently infected patients in both sets. The logistic stepwise regression analysis of WB method shows sensitivity, specificity, and accuracy of 93.02%, 92.11%, and 92.59%. For BED-CEIA, they were 76.74%, 86.84%, and 81.48%. In the validation set, overall agreement rate, sensitivity, and specificity were 88.46%, 84.78%, and 86.11% in the WB-based method and 50.00%, 84.78%, and 72.22% in the BED-CEIA method. Conclusions WB-based method is a promising approach to predict recent HIV-1 infection, especially in resource-limited regions.

Opiate use inhibits TLR9 signaling pathway in vivo : possible role in pathogenesis of HIV-1 infection

The molecular mechanism of opiate use promoting HIV-1 infection is not fully understood. TLR9 is expressed in many immune cells, including monocytes, macrophages, which can recognize viruses and viral products and consequently induce the production of antiviral factors and initiate immune responses. Previous studies have shown that chronic viral infections can overcome and impair TLR9 pathway. We aimed to explore whether opiate use enhances HIV infection through inhibition of TLR9 pathway via a population-based study. A total of 200 subjects were enrolled and divided into four groups as follows: Opiate+ HIV+ (50), Opiate− HIV+ (50), Opiate+ HIV− (50), and healthy control (Opiate− HIV−, 50). All HIV-infected subjects did not receive antiretroviral therapy while they were enrolled in the study. The results showed that opiate use was associated with higher viral load and lower CD4+ T cell count. Opiate use alone led to lower expression of TLR9, IRF7, and IFN-α at the protein level in PBMCs. Combined with HIV-1 infection, opiate use resulted in lower expression of MyD88, ISG56, and MxA. In addition, morphine treatment promoted HIV-1 replication in macrophages via inhibition of TLR9 pathway. Our data reveal that opiate use plays a cofactor role in pathogenesis of HIV-1 infection through inhibition of TLR9 pathway.

Commercial Sexual Behaviors Among Male Rural-to-Urban Migrants in Western China: Implications for HIV Prevention.

Rural-to-urban migrants are at high risk of HIV infection. The goal of this survey was to explore the commercial sexual behavior and condom use among male rural-to-urban migrants in western China. A cross-sectional survey on male rural-to-urban migrants in western China was conducted. Among all the subjects surveyed, 140 (7.4%) had commercial sexual behavior, which is associated with being aged older than 24 years, being of Han or other ethnic minorities, being divorced, separated, or widowed, having experienced drug abuse, having had heterosexual behavior, having had casual sexual partners, having had sex with a homosexual, and being from Xinjiang. A total of 31.4% of them never use condoms when buying sex. Not using condoms is associated with being from Chongqing, having a high school or above education, and having commercial sex monthly. Commercial sexual behavior and not using condoms are common among male rural-to-urban migrants in western China. Strategies and appropriate education should be developed to prevent HIV transmission due to high-risk sexual behaviors.

A New Hybrid Model Using an Autoregressive Integrated Moving Average and a Generalized Regression Neural Network for the Incidence of Tuberculosis in Heng County, China

It is a daunting task to eradicate tuberculosis completely in Heng County due to a large transient population, human immunodeficiency virus/tuberculosis coinfection, and latent infection. Thus, a high-precision forecasting model can be used for the prevention and control of tuberculosis. In this study, four models including a basic autoregressive integrated moving average (ARIMA) model, a traditional ARIMA-generalized regression neural network (GRNN) model, a basic GRNN model, and a new ARIMA-GRNN hybrid model were used to fit and predict the incidence of tuberculosis. Parameters including mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) were used to evaluate and compare the performance of these models for fitting historical and prospective data. The new ARIMA-GRNN model had superior fit relative to both the traditional ARIMA-GRNN model and basic ARIMA model when applied to historical data and when used as a predictive model for forecasting incidence during the subsequent 6 months. Our results suggest that the new ARIMA-GRNN model may be more suitable for forecasting the tuberculosis incidence in Heng County than traditional models.