Severe fever with thrombocytopenia syndrome, which results in severe illness and has a high case-fatality rate, is caused by a novel bunyavirus, severe fever with thrombocytopenia syndrome virus. We found that samples from 2/237 (0.8%) healthy persons and 111/134 (83%) goats in Yiyuan County, Shandong Province, China, were seropositive for this virus.

BackgroundHand, foot, and mouth disease (HFMD) has caused major public health concerns worldwide, and has become one of the leading causes of children death. China is the most serious epidemic area with a total of 3,419,149 reported cases just from 2008 to 2010, and its different geographic areas might have different spatial epidemiology characteristics at different spatial-temporal scale levels. We conducted spatial and spatial-temporal epidemiology analysis to HFMD at county level in Shandong Province, China.MethodsBased on the China National Disease Surveillance Reporting and Management System, the spatial-temporal database of HFMD from 2007 to 2011 was built. The global autocorrelation statistic (Moran’s I) was first used to detect the spatial autocorrelation of HFMD cases in each year. Purely Spatial scan statistics combined with Space-time scan statistic were used to detect epidemic clusters.ResultsThe annual average incidence rate was 93.70 per 100,000 in Shandong Province. Most HFMD cases (93.94%) were aged within 0–5 years old with an average male-to-female sex ratio 1.71, and the incidence seasonal peak was between April and July. The dominant pathogen was EV71 (47.35%), and CoxA16 (26.59%). HFMD had positive spatial autocorrelation at medium spatial scale level (county level) with higher Moran’s I from 0.31 to 0.62 (P<0.001). Seven spatial-temporal clusters were detected from 2007 to 2011 in the landscape of the whole Shandong, with EV71 or CoxA16 as the dominant pathogen for most hotspots areas.ConclusionsThe spatial-temporal clusters of HFMD wandered around the whole Shandong Province during 2007 to 2011, with EV71 or CoxA16 as the dominant pathogen. These findings suggested that a real-time spatial-temporal surveillance system should be established for identifying high incidence region and conducting prevention to HFMD timely.

Introduction Previous metabolomics studies have revealed perturbed metabolic signatures in esophageal squamous cell carcinoma (ESCC) patients, however, most of these studies included mainly late-staged ESCC patients due to the difficulties of collecting the early-staged samples from asymptotic ESCC subjects. Objectives This study aims to explore the early-staged ESCC metabolic signatures and potential of serum metabolomics to diagnose ESCC at early stages. Methods Serum samples of 97 ESCC patients (stage 0, 39 cases; stage I, 17 cases; stage II, 11 cases, stage III, 30 cases) and 105 healthy controls (HC) were enrolled and randomly separated into training data (77 ESCCs, 84 HCs) and validation data (20 ESCCs, 21 HCs). Untargeted metabolomics was performed to identify ESCC-related metabolic signatures. ResultsThe global metabolomics profiles could clearly distinguish ESCC from HC in training data. 16 ascertained metabolites were found to be disturbed in the metabolic pathways characterized by dysregulated fatty acid biosynthesis, glycerophospholipid metabolism, choline metabolism in cancer and linoleic acid metabolism. The AUC value in validation data was 0.895, with sensitivity 85.0 % and specificity 90.5 %. Good diagnostic performances were also achieved for early stage ESCC, with the values of area under the curve (AUC) 0.881 for the ESCC patients in both stage 0 and I-II. In addition, six metabolites were found to discriminate ESCC stages. Among them, three biomarkers, dodecanoic acid, LysoPA(18:1), and LysoPC(14:0), exhibited clear trend for ESCC progression. Conclusion These findings suggest serum metabolomics, performed in a minimally noninvasive and convenient manner, may possess great potential for early diagnosis of ESCC patients.

Background Haemaphysalis longicornis, a vector of various pathogens with medical and veterinary importance, is native to eastern Asia, and recently reached the USA as an emerging disease threat. In this study, we aimed to identify the geographical distribution, hosts, and associated pathogens of H longicornis.Methods Data were collected from multiple sources, including a field survey, reference book, literature review, and related websites. The thematic maps showing geographical distribution of H longicornis and associated pathogens were produced by ArcGIS. Hosts of H longicornis and positive rates for H longicornis-associated pathogens were estimated by meta-analysis. Ecological niche modelling was used to predict potential global distribution of H longicornis.Findings H longicornis was found to be present in ten countries, predominantly in eastern Asia, the USA, Australia, and New Zealand. The tick was known to feed on a variety of domestic and wild animals, and humans. At least 30 human pathogens were associated with H longicornis, including seven species of spotted fever group rickettsiae, seven species in the family of Anaplasmataceae, four genospecies in the complex Borrelia burgdorferi sensu lato, two Babesia species, six species of virus, and Francisella, Bartonella, Coxiella, and Toxoplasma, which were mainly reported in eastern Asia. The predictive modelling revealed that H longicornis might affect more extensive regions, including Europe, South America, and Africa, where the tick has never been recorded before.Interpretation H longicornis is relatively common in the world, and is associated with various human and animal pathogens. Authorities and health-care workers should be aware of the threat of the tick species to public health and veterinary medicine. Surveillance and further investigations should be enhanced globally.

ObjectivesIt remains unclear whether non-alcoholic fatty liver disease (NAFLD) is a cause or a consequence of metabolic syndrome (MetS). We proposed a simplified Bayesian network (BN) and attempted to confirm their reciprocal causality.SettingBidirectional longitudinal cohorts (subcohorts A and B) were designed and followed up from 2005 to 2011 based on a large-scale health check-up in a Chinese population.ParticipantsSubcohort A (from NAFLD to MetS, n=8426) included the participants with or without NAFLD at baseline to follow-up the incidence of MetS, while subcohort B (from MetS to NAFLD, n=16 110) included the participants with or without MetS at baseline to follow-up the incidence of NAFLD.ResultsIncidence densities were 2.47 and 17.39 per 100 person-years in subcohorts A and B, respectively. Generalised estimating equation analyses demonstrated that NAFLD was a potential causal factor for MetS (relative risk, RR, 95% CI 5.23, 3.50 to 7.81), while MetS was also a factor for NAFLD (2.55, 2.23 to 2.92). A BN with 5 simplification strategies was used for the reciprocal causal inference. The BN's causal inference illustrated that the total effect of NAFLD on MetS (attributable risks, AR%) was 2.49%, while it was 19.92% for MetS on NAFLD. The total effect of NAFLD on MetS components was different, with dyslipidemia having the greatest (AR%, 10.15%), followed by obesity (7.63%), diabetes (3.90%) and hypertension (3.51%). Similar patterns were inferred for MetS components on NAFLD, with obesity having the greatest (16.37%) effect, followed by diabetes (10.85%), dyslipidemia (10.74%) and hypertension (7.36%). Furthermore, the most important causal pathway from NAFLD to MetS was that NAFLD led to elevated GGT, then to MetS components, while the dominant causal pathway from MetS to NAFLD began with dyslipidaemia.ConclusionsThe findings suggest a reciprocal causality between NAFLD and MetS, and the effect of MetS on NAFLD is significantly greater than that of NAFLD on MetS.

BackgroundTuberculosis (TB) remains a major public health burden in many developing countries. China alone accounted for an estimated 12% of all incident TB cases worldwide in 2010. Several studies showed that the spatial distribution of TB was nonrandom and clustered. Thus, a spatial analysis was conducted with the aim to explore the spatial epidemiology of TB in Linyi City, which can provide guidance for formulating regional prevention and control strategies.MethodsThe study was based on the reported cases of TB, between 2005 and 2010. 35,308 TB cases were geo-coded at the town level (n = 180). The spatial empirical Bayes smoothing, spatial autocorrelation and space-time scan statistic were used in this analysis.ResultsSpatial distribution of TB in Linyi City from 2005 to 2010 was mapped at town level in the aspects of crude incidence, excess hazard and spatial smoothed incidence. The spatial distribution of TB was nonrandom and clustered with the significant Moran’s I for each year. Local Gi* detected five significant spatial clusters for high incidence of TB. The space-time analysis identified one most likely cluster and nine secondary clusters for high incidence of TB.ConclusionsThere is evidence for the existence of statistically significant TB clusters in Linyi City, China. The result of this study may assist health departments to develop a better preventive strategy and increase the public health intervention’s effectiveness.

Spatial panel data models are useful when longitudinal data with multiple units are available and spatial autocorrelation exists. The association found between HFMD and meteorological factors makes a contribution towards advancing knowledge with respect to the causality of HFMD and has policy implications for HFMD prevention and control.

Background This longitudinal study aims to characterize longitudinal body mass index ( BMI ) trajectories during young adulthood (20–40 years) and examine the impact of level‐independent BMI trajectories on hypertension risk. Methods and Results The cohort consisted of 3271 participants (1712 males and 1559 females) who had BMI and blood pressure ( BP ) repeatedly measured 4 to 11 times during 2004 to 2015 and information on incident hypertension. Four distinct trajectory groups were identified using latent class growth mixture model: low‐stable (n=1497), medium‐increasing (n=1421), high‐increasing (n=291), sharp‐increasing (n=62). Model‐estimated levels and linear slopes of BMI at each age point between ages 20 and 40 were calculated in 1‐year intervals using the latent class growth mixture model parameters and their first derivatives, respectively. Compared with the low‐stable group, the hazard ratios and 95% CI were 2.42 (1.88, 3.11), 4.25 (3.08, 5.87), 11.17 (7.60, 16.41) for the 3 increasing groups, respectively. After adjusting for covariates, the standardized odds ratios and 95% CI of model‐estimated BMI level for incident hypertension increased in 20 to 35 years, ranging from 0.80 (0.72–0.90) to 1.59 (1.44–1.75); then decreased gradually to 1.54 (1.42–1.68). The standardized odds ratio s of level‐adjusted linear slopes increased from 1.22 (1.09–1.37) to 1.79 (1.59–2.01) at 20 to 24 years; then decreased rapidly to 1.12 (0.95–1.32). Conclusions These results indicate that the level‐independent BMI trajectories during young adulthood have significant impact on hypertension risk. Age between 20 and 30 years is a crucial period for incident hypertension, which has implications for early prevention.