Objective To investigate the effect of LI-cadherin- SiRNA protein on the growth and metastatic potentials of transplanted human hepatocellular carcinoma cell lines (Hep3B) in nude mice.Methods We transfected LI-cadherin- SiRNA to Hep3B cells,Hep3B cell suspension (transfected or control ) was injected subcapsullaryly into the spleen of nude mice,hepatic metastasis was observed by naked eye and immunohistochemistry.In addition,Western-blot was used to detect the level of LI-cadherin in different metastatic site.Results (1) Hep3B was green after successful transfect interference vector under fluorescence inverted microscope,in this study,the transfect rate was 80％.(2) Hep3B liver metastasis model in nude mice was established.The metastasis rates in the empty plasmid carrying group,the control group and the SiRNA transfect group were 50％,60％ and 80％,respectively.The number of metastasis caner nodules in the SiRNA transfect group was 26,significantly higher than other two groups.(3) The level of protein expression for LI-cadherin in the SiRNA transfect group is significantly lower than the control group and the empty plasmid carrying group.Conclusions LI-cadherin is crucial and important for the adhension capability of HCC in its migration.SiRNA transfected LI-cadherin increases the metastasis in a nude mouse model inoculated with human hepatocellular carcinoma cell lines.

Objective:To propose a novel neural network to achieve tooth instance segmentation and recognition based on cone-beam computed tomography(CBCT)voxel data.Methods:The proposed methods included three different convolutional neural network models.The architecture was based on the Resnet module and built according to the structure of"Encoder-Decoder"and U-Net.The CBCT image was de-sampled and a fixed-size region of interest(ROI)containing all the teeth was determined.ROI would first through a two-branch"encoder and decoder"structure of the network,the network could pre-dict each voxel in the input data of the spatial embedding.The post-processing algorithm would cluster the prediction results of the relevant spatial location information according to the two-branch network to realize the tooth instance segmentation.The tooth position identification was realized by another U-Net model based on the multi-classification segmentation task.According to the predicted results of the net-work,the post-processing algorithm would classify the tooth position according to the voting results of each tooth instance segmentation.At the original spatial resolution,a U-Net network model for the fine-tooth segmentation was trained using the region corresponding to each tooth as the input.According to the results of instance segmentation and tooth position identification,the model would process the correspon-ding positions on the high-resolution CBCT images to obtain the high-resolution tooth segmentation re-sults.In this study,CBCT data of 59 cases with simple crown prostheses and implants were collected for manual labeling as the database,and statistical indicators were evaluated for the prediction results of the algorithm.To assess the performance of tooth segmentation and classification,instance Dice similarity co-efficient(IDSC)and the average Dice similarity coefficient(ADSC)were calculated.Results:The ex-perimental results showed that the IDSC was 89.35％,and the ADSC was 84.74％.After eliminating the data with prostheses artifacts,the database of 43 samples was generated,and the performance of the training network was better,with 90.34％for IDSC and 87.88％for ADSC.The framework achieved ex-cellent performance on tooth segmentation and identification.Voxels near intercuspation surfaces and fuzzy boundaries could be separated into correct instances by this framework.Conclusion:The results show that this method can not only successfully achieve 3D tooth instance segmentation but also identify all teeth notation numbers accurately,which has clinical practicability.