Intrauterine hepatitis B virus (HBV) infection has been suggested to be caused by transplacental transmission that cannot be blocked by hepatitis B vaccine. This would decrease the effectiveness of hepatitis B vaccine. This study examined the risk factors and mechanism of transplacental HBV transmission. A case-control study included 402 newborn infants from 402 HBsAg-positive pregnant women. Among these, 15 newborn infants infected with HBV by intrauterine transmission were selected as cases, and the rest as controls. A pathology study included 101 full-term placentas from the HBsAg-positive pregnant women above and 14 from HBsAg-negative pregnant women. Immunohistochemistry staining and HBV DNA in situ hybridization were used to estimate the association of intrauterine HBV infection and HBV infection in the placentas. HBeAg positivity in mothers' sera (OR = 17.07, 95%CI 3.39-86.01) and threatened preterm labor (OR = 5.44, 95%CI 1.15-25.67) were found to be associated with transplacental HBV transmission. The intrauterine infection rate increased linearly and significantly with maternal serum HBsAg titers (trend test P = 0.0117) and HBV DNA concentration (trend test P < 0.01). Results of the pathology study showed that HBV infection rates decreased gradually from the maternal side to the fetal side (trend test P = 0.0009) in the placental cell layers. There was a significant association between intrauterine HBV transmission and HBV infection in villous capillary endothelial cells (VCEC) in the placenta (OR = 18.46, P = 0.0002). The main risk factors for intrauterine HBV infection are maternal serum HBeAg positivity, history of threatened preterm labor, and HBV in the placenta especially the villous capillary endothelial cells. Previous reports of transplacental leakage of maternal blood causing intrauterine infection are confirmed. In addition, there appears to be a "cellular transfer" of HBV from cell to cell in the placenta causing intrauterine infection. This latter hypothesis needs to be confirmed.

Background: The triglyceride-glucose index (TyG index) has been regarded as a reliable alternative marker of insulin resistance and an independent predictor of cardiovascular outcomes. Whether the TyG index predicts adverse cardiovascular events in patients with diabetes and acute coronary syndrome (ACS) remains uncertain. The aim of this study was to investigate the prognostic value of the TyG index in patients with diabetes and ACS. Methods: A total of 2531 consecutive patients with diabetes who underwent coronary angiography for ACS were enrolled in this study. Patients were divided into tertiles according to their TyG index. The primary outcomes included the occurrence of major adverse cardiovascular events (MACEs), defined as all-cause death, non-fatal myocardial infarction and non-fatal stroke. The TyG index was calculated as the ln (fasting triglyceride level [mg/dL] × fasting glucose level [mg/dL]/2). Results: The incidence of MACE increased with TyG index tertiles at a 3-year follow-up. The Kaplan-Meier curves showed significant differences in event-free survival rates among TyG index tertiles (P = 0.005). Multivariate Cox hazards regression analysis revealed that the TyG index was an independent predictor of MACE (95% CI 1.201-1.746; P < 0.001). The optimal TyG index cutoff for predicting MACE was 9.323 (sensitivity 46.0%; specificity 63.6%; area under the curve 0.560; P = 0.001). Furthermore, adding the TyG index to the prognostic model for MACE improved the C-statistic value (P = 0.010), the integrated discrimination improvement value (P = 0.001) and the net reclassification improvement value (P = 0.019). Conclusions: The TyG index predicts future MACE in patients with diabetes and ACS independently of known cardiovascular risk factors, suggesting that the TyG index may be a useful marker for risk stratification and prognosis in patients with diabetes and ACS.

BackgroundPredicting drug side effects is an important topic in the drug discovery. Although several machine learning methods have been proposed to predict side effects, there is still space for improvements. Firstly, the side effect prediction is a multi-label learning task, and we can adopt the multi-label learning techniques for it. Secondly, drug-related features are associated with side effects, and feature dimensions have specific biological meanings. Recognizing critical dimensions and reducing irrelevant dimensions may help to reveal the causes of side effects.MethodsIn this paper, we propose a novel method ‘feature selection-based multi-label k-nearest neighbor method’ (FS-MLKNN), which can simultaneously determine critical feature dimensions and construct high-accuracy multi-label prediction models.ResultsComputational experiments demonstrate that FS-MLKNN leads to good performances as well as explainable results. To achieve better performances, we further develop the ensemble learning model by integrating individual feature-based FS-MLKNN models. When compared with other state-of-the-art methods, the ensemble method produces better performances on benchmark datasets.ConclusionsIn conclusion, FS-MLKNN and the ensemble method are promising tools for the side effect prediction. The source code and datasets are available in the Additional file 1.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0774-y) contains supplementary material, which is available to authorized users.

Plant roots and soil microorganisms interact with each other mainly in the rhizosphere. Changes in the community structure of the rhizosphere microbiome are influenced by many factors. In this study, we determined the community structure of rhizosphere bacteria in cotton, and studied the variation of rhizosphere bacterial community structure in different soil types and developmental stages using TM-1, an upland cotton cultivar (Gossypium hirsutum L.) and Hai 7124, a sea island cotton cultivar (G. barbadense L.) by high-throughput sequencing technology. Six bacterial phyla were found dominantly in cotton rhizosphere bacterial community including Acidobacteria, Actinobacteria, Bacteroidetes, Planctomycetes, Proteobacteria, and Verrucomicrobia. The abundance of Acidobacteria, Cyanobacteria, Firmicutes, Planctomycetes and Proteobacteria were largely influenced by cotton root. Bacterial α-diversity in rhizosphere was lower than that of bulk soil in nutrient-rich soil, but higher in cotton continuous cropping field soil. The β-diversity in nutrient-rich soil was greater than that in continuous cropping field soil. The community structure of the rhizosphere bacteria varied significantly during different developmental stages. Our results provided insights into the dynamics of cotton rhizosphere bacterial community and would facilitate to improve cotton growth and development through adjusting soil bacterial community structure artificially.

SUMMARYAtFes1A is induced by high temperatures, and encodes a protein containing the armadillo repeat motif. Little is known about its biological function, however. In this study, we observed an increased heat-sensitive phenotype in atfes1a mutants, suggesting the involvement of AtFes1A in acquired thermotolerance. We found that AtFes1A is cytosolic and associates with cytosolic Hsp70. Loss of AtFes1A leads to a selective reduction of cytosolic Hsp70 and a global increase in heat shock transcription. Thus, AtFes1A appears to prevent cytosolic Hsp70 degradation, and acts as a negative regulator of heat-shock transcription. We also found increased ubiquitination of total protein in atfes1a mutants after severe heat stress. These findings suggest that AtFes1A plays an important role in heat response signalling pathways, in addition to its role in thermotolerance.