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ObjectivesXinjiang is one of the high TB burden provinces of China. A spatial analysis was conducted using geographical information system (GIS) technology to improve the understanding of geographic variation of the pulmonary TB occurrence in Xinjiang, its predictors, and to search for targeted interventions.MethodsNumbers of reported pulmonary TB cases were collected at county/district level from TB surveillance system database. Population data were extracted from Xinjiang Statistical Yearbook (2006~2014). Spatial autocorrelation (or dependency) was assessed using global Moran’s I statistic. Anselin’s local Moran’s I and local Getis-Ord statistics were used to detect local spatial clusters. Ordinary least squares (OLS) regression, spatial lag model (SLM) and geographically-weighted regression (GWR) models were used to explore the socio-demographic predictors of pulmonary TB incidence from global and local perspectives. SPSS17.0, ArcGIS10.2.2, and GeoDA software were used for data analysis.ResultsIncidence of sputum smear positive (SS+) TB and new SS+TB showed a declining trend from 2005 to 2013. Pulmonary TB incidence showed a declining trend from 2005 to 2010 and a rising trend since 2011 mainly caused by the rising trend of sputum smear negative (SS-) TB incidence (p<0.0001). Spatial autocorrelation analysis showed the presence of positive spatial autocorrelation for pulmonary TB incidence, SS+TB incidence and SS-TB incidence from 2005 to 2013 (P <0.0001). The Anselin’s Local Moran’s I identified the “hotspots” which were consistently located in the southwest regions composed of 20 to 28 districts, and the “coldspots” which were consistently located in the north central regions consisting of 21 to 27 districts. Analysis with the Getis-Ord Gi* statistic expanded the scope of “hotspots” and “coldspots” with different intensity; 30 county/districts clustered as “hotspots”, while 47 county/districts clustered as “coldspots”. OLS regression model included the “proportion of minorities” and the “per capita GDP” as explanatory variables that explained 64% the variation in pulmonary TB incidence (adjR2 = 0.64). The SLM model improved the fit of the OLS model with a decrease in AIC value from 883 to 864, suggesting “proportion of minorities” to be the only statistically significant predictor. GWR model also improved the fitness of regression (adj R2 = 0.68, AIC = 871), which revealed that “proportion of minorities” was a strong predictor in the south central regions while “per capita GDP” was a strong predictor for the southwest regions.ConclusionThe SS+TB incidence of Xinjiang had a decreasing trend during 2005–2013, but it still remained higher than the national average in China. Spatial analysis showed significant spatial autocorrelation in pulmonary TB incidence. Cluster analysis detected two clusters—the “hotspots”, which were consistently located in the southwest regions, and the “coldspots”, which were consistently located in the north central regions. The exploration of socio-demographic predictors identified t...

Negative-stranded RNA viruses cover their genome with nucleoprotein (N) to protect it from the human innate immune system. Abrogation of the function of N offers a unique opportunity to combat the spread of the viruses. Here, we describe a unique fold of N from Leanyer virus (LEAV, Orthobunyavirus genus, Bunyaviridae family) in complex with single-stranded RNA refined to 2.78 Å resolution as well as a 2.68 Å resolution structure of LEAV N-ssDNA complex. LEAV N is made up of an N-and a C-terminal lobe, with the RNA binding site located at the junction of these lobes. The LEAV N tetramer binds a 44-nucleotide-long single-stranded RNA chain. Hence, oligomerization of N is essential for encapsidation of the entire genome and is accomplished by using extensions at the N and C terminus. Molecular details of the oligomerization of N are illustrated in the structure where a circular ring-like tertiary assembly of a tetramer of LEAV N is observed tethering the RNA in a positively charged cavity running along the inner edge. Hydrogen bonds between N and the C2 hydroxyl group of ribose sugar explain the specificity of LEAV N for RNA over DNA. In addition, base-specific hydrogen bonds suggest that some regions of RNA bind N more tightly than others. Hinge movements around F20 and V125 assist in the reversal of capsidation during transcription and replication of the virus. Electron microscopic images of the ribonucleoprotein complexes of LEAV N reveal a filamentous assembly similar to those found in phleboviruses.crystal structure | nucleoprotein complex with ssRNA | RNP

ObjectivesXinjiang is one of the highest TB-burdened provinces of China. A time-series analysis was conducted to evaluate the trend, seasonality of active TB in Xinjiang, and explore the underlying mechanism of TB seasonality by comparing the seasonal variations of different subgroups.MethodsMonthly active TB cases from 2005 to 2014 in Xinjiang were analyzed by the X-12-ARIMA seasonal adjustment program. Seasonal amplitude (SA) was calculated and compared within the subgroups.ResultsA total of 277,300 confirmed active TB cases were notified from 2005 to 2014 in Xinjiang, China, with a monthly average of 2311±577. The seasonality of active TB notification was peaked in March and troughed in October, with a decreasing SA trend. The annual 77.31% SA indicated an annual mean of additional TB cases diagnosed in March as compared to October. The 0–14-year-old group had significantly higher SA than 15–44-year-old group (P<0.05). Students had the highest SA, followed by herder and migrant workers (P<0.05). The pleural TB cases had significantly higher SA than the pulmonary cases (P <0.05). Significant associations were not observed between SA and sex, ethnic group, regions, the result of sputum smear microcopy, and treatment history (P>0.05).ConclusionTB notification in Xinjiang shows an apparent seasonal variation with a peak in March and trough in October. For the underlying mechanism of TB seasonality, our results hypothesize that winter indoor crowding increases the risk of TB transmission, and seasonality was mainly influenced by the recent exogenous infection rather than the endogenous reactivation.

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In this study, plasma-free amino acid profiles were used to investigate pre-cancerous cervical intraepithelial neoplasia (CIN) and cervical squamous cell carcinoma (CSCC) metabolic signatures in plasma. Additionally, the diagnostic potential of these profiles was assessed, as well as their ability to provide novel insight into CSCC metabolism and systemic effects. Plasma samples from CIN patients (n = 26), CSCC patients (n = 22), and a control healthy group (n = 35) were analyzed by high-performance liquid chromatography, and their spectral profiles were subjected to the t test for statistical significance. Potential metabolic biomarkers were identified using database comparisons that examine the significance of metabolites. Compared with healthy controls, patients with CIN and CSCC demonstrated lower levels of plasma amino acids; plasma levels of arginine and threonine were increased in CIN patients but were decreased in cervical cancer patients. Additionally, the levels of a larger group of amino acids (aspartate, glutamate, asparagine, serine, glycine, histidine, taurine, tyrosine, valine, methionine, lysine, isoleucine, leucine, and phenylalanine) were gradually reduced from CIN to invasive cancer. These findings suggest that plasma-free amino acid profiling has great potential for improving cancer screening and diagnosis and for understanding disease pathogenesis. Plasma-free amino acid profiles may have the potential be used to determine cancer diagnoses in the early stage from a single blood sample and may enhance our understanding of its mechanisms.

Xinjiang Uyghur Autonomous Region Health Bureau.