The regulation of a target gene expression is very important in gene therapy. However, constitutive or inappropriate expression of the genes with traditional expression system may interfere with the effect of the gene therapy, even may lead to lethal side effect. We constructed an RU486 inducible eukaryotic vector carrying DsRed protein and evaluated its regulatable effect in vitro. The single vector named PDC-RURED was constructed with molecular biological methods, which contained DsRed gene, promoter and RU486-inducible system. To minimize any potential interference, we spaced the two transcriptional elements with a 1.6 kb insulator. The vector was identified by different enzyme restrictions, sequencing analysis and PCR assay. We demonstrated the regulatable expression of this vector after transfection in HEK293 cells by fluorescence microscopy and flow cytometry. In the absence of RU486, no significant DsRed protein activation was observed, whereas in the presence of RU486 up to 40 fold activation of the DsRed protein was observed in cultured cells. The data show that the novel eukaryotic expression plasmid vector can be used to regulate the expression level of genes of interest in appropriate time under the control of RU486. This inducible expression vector provides a powerful tool for the research of gene regulation and gene therapy.
Cell Line ; Fluorescent Dyes ; metabolism ; Genetic Therapy ; methods ; Genetic Vectors ; genetics ; Humans ; Kidney ; cytology ; Luminescent Proteins ; biosynthesis ; genetics ; Mifepristone ; pharmacology ; Promoter Regions, Genetic ; genetics ; Transfection

A group of 19 referred hypertensive patients were diagnosed to have primary aldosteronism(PA) with inconclusive computed tomography scan results.Adrenal vein sarmpling (AVS) was performed in all patients.AVS was successful in 16 cases but failed in 3 cases.According to the results of AVS and postoperative pathology,8 cases were diagnosed as aldosterone-producing adenoma (APA) and unilateral adrenal hyperplasia (UAH),and the other 8 cases were diagnosed as idiopathic hyperaldosteronism (IHA).In conclusion,AVS is one of the most crucial methods in typing diagnosis of PA.

Objective     To recognize the different phases of Korotkoff sounds through deep learning technology, so as to improve the accuracy of blood pressure measurement in different populations. Methods     A classification model of the Korotkoff sounds phases was designed, which fused attention mechanism (Attention), residual network (ResNet) and bidirectional long short-term memory (BiLSTM). First, a single Korotkoff sound signal was extracted from the whole Korotkoff sounds signals beat by beat, and each Korotkoff sound signal was converted into a Mel spectrogram. Then, the local feature extraction of Mel spectrogram was processed by using the Attention mechanism and ResNet network, and BiLSTM network was used to deal with the temporal relations between features, and full-connection layer network was applied in reducing the dimension of features. Finally, the classification was completed by SoftMax function. The dataset used in this study was collected from 44 volunteers (24 females, 20 males with an average age of 36 years), and the model performance was verified using 10-fold cross-validation. Results     The classification accuracy of the established model for the 5 types of Korotkoff sounds phases was 93.4%, which was higher than that of other models. Conclusion     This study proves that the deep learning method can accurately classify Korotkoff sounds phases, which lays a strong technical foundation for the subsequent design of automatic blood pressure measurement methods based on the classification of the Korotkoff sounds phases.