BackgroundNeither HBV DNA nor HBsAg positivity at birth is an accurate marker for HBV infection of infants. No data is available for continuous changes of HBV markers in newborns to HBsAg(+) mothers. This prospective, multi-centers study aims at observing the dynamic changes of HBV markers and exploring an early diagnostic marker for mother-infant infection.MethodsOne hundred forty-eight HBsAg(+) mothers and their newborns were enrolled after mothers signed the informed consent forms. Those infants were received combination immunoprophylaxis (hepatitis B immunoglobulin [HBIG] and hepatitis B vaccine) at birth, and then followed up to 12 months. Venous blood of the infants (0, 1, 7, and 12 months of age) was collected to test for HBV DNA and HBV markers.ResultsOf the 148 infants enrolled in our study, 41 and 24 infants were detected as HBsAg(+) and HBV DNA(+) at birth, respectively. Nine were diagnosed with HBV infection after 7 mo follow-up. Dynamic observation of the HBV markers showed that HBV DNA and HBsAg decreased gradually and eventually sero-converted to negativity in the non-infected infants, whereas in the infected infants, HBV DNA and HBsAg were persistently positive, or higher at the end of follow-up. At 1 mo, the infants with anti-HBs(+), despite positivity for HBsAg or HBV DNA at birth, were resolved after 12 mo follow-up, whereas all the nine infants with anti-HBs(−) were diagnosed with HBV infection. Anti-HBs(−) at 1 mo showed a higher positive likelihood ratio for HBV mother-infant infection than HBV DNA and/or HBsAg at birth.ConclusionsNegativity for anti-HBs at 1 mo can be considered as a sensitive and early diagnostic indictor for HBV infection in the infants with positive HBV DNA and HBsAg at birth, especially for those infants with low levels of HBV DNA load and HBsAg titer.

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BackgroundLong noncoding RNAs play important roles in the development of various diseases. This study aimed to evaluate the effects and mechanism of VIM antisense RNA 1 (VIM-AS1) in the development of preeclampsia.Material/MethodsHTR-8/SVneo cells were divided into normal control (NC), Model, Blank, and VIM-AS1 groups. These groups were analyzed for their VIM-AS1 gene expressions by RT-PCR, HTR-8/SVneo cell invasion was assessed by transwell and migration by wound healing, cell morphology was assessed by microscopy examination, and E-cadherin, Snail, and Vimentin genes expressions were assessed by RT-PCR and WB assay.ResultsVIM-AS1 gene expression was significantly different among normal placenta tissue, mild preeclampsia tissues, and severe preeclampsia tissues (P<0.001 or P<0.01). VIM-AS1 gene expressions, cell invasions, and wound healing rates in the Model and Blank groups were significantly suppressed compared with that of NC group (P<0.001, all). With VIM-AS1 supplementation, VIM-AS1 gene expression, cell invasion, and wound healing rate in the VIM-AS1 group were significantly increased compared with that in the Model group (P<0.001). RT-PCR and WB assay showed that E-cadherin gene and protein expressions in Model and Blank groups were significantly upregulated compared with the NC group (P<0.001); Snail and Vimentin gene and protein expressions in the Model and Blank groups were significantly downregulated compared with the NC group (P<0.001). With VIM-AS1 supplementation, E-cadherin, Snail, and Vimentin gene and proteins expression levels in the VIM-AS1 group were significantly different compared with that in the Model group (P<0.001).ConclusionsVIM-AS1 promotes preeclampsia via inducing epithelial-to-mesenchymal transition (EMT).

Remote sensing scene classification aims to automatically assign a specific semantic label to each image. It is challenging to classify remote sensing scene images due to the images' diversity and rich spatial information. Recently, convolutional neural networks have been widely used to overcome these difficulties, such as the famous Visual Geometry Group (VGG) network. However, the VGG network with local receptive fields cannot model the global information of remote sensing images well. It also needs a large number of parameters and floating point operations to achieve satisfactory accuracy. To overcome these challenges, we introduce the self-attention mechanism to the VGG network. Specifically, we replace the last four convolutional layers in the VGG-19 network with two cascaded self-attention blocks, each consisting of two multi-head self-attention (MHSA) layers with the residual network structure. The new structure can simultaneously explore the local and global information from remote sensing scenes. Such improvements not only reduce model parameters but also improve the classification performance. The effectiveness of the proposed method is validated through experiments on four public data sets, i.e., NaSC-TG2, WHU-RS19, AID and EuroSAT.