The key factor responsible for the slowness of registration of medical devices in China is clinical trial. The clinical trial in European and U.S. is taken as an example to analyze the problems in clinical trials in China. Some suggestions are proposed to solve the problem.
China ; Clinical Trials as Topic ; Device Approval ; Research Design

Objective:To explore the value of chest low-dose CT (LDCT) in post-discharge follow-up assessments of patients with coronavirus disease 2019 (COVID-19).Methods:The chest CT findings of 58 patients with COVID-19 from March 17 to March 25, 2020 at Remin Hospital of Wuhan University were retrospectively analyzed. Two radiologists independently scored the subjective image quality on a 5-point Likert scale. The signal-to-noise ratio (SNR) and SD air of images and the CT radiation dose parameters were calculated, including the CT volume dose index (CTDI vol), dose length product (DLP), and effective radiation dose ( E). Results:The subjective image quality scores on CT images obtained before and after discharge by readers 1 and 2, were 4.45±0.22, 3.88±0.33 ( P>0.05) and 4.37±0.18, 3.91±0.35 ( P>0.05), respectively. The SNR and SD air in LDCT after discharge were 4.39±0.95 and 7.19±2.41, which were significantly lower than those in routine chest CT before discharge (5.14±1.06, Z=-5.551, P<0.001; 6.48±1.57, Z=-3.217, P<0.001). All of the obtained images were sufficient for diagnosis. The CTDI vol, DLP, and E in LDCT were significantly lower than those in routine CT [(2.41±0.09), (10.53±1.03)mGy, Z=-6.568, P<0.001; (88.03±5.33), (338.74±34.64)mGy·cm, Z=-6.624, P<0.001; and (1.23±0.17), (4.74±0.48)mSv, Z=-5.976, P<0.001]. Conclusions:Patients with COVID-19 can be followed up with low-dose chest CT after discharge.

Objective to establish a combined diagnosis model of scleroderma related genes based on gene expression comprehensive database(GEO)and artificial neural network(ANN)and to evaluate its effect and to predict and analyze targeted traditional Chinese medicine.Methods two scleroderma chips GSE23741 and GSE95065 were obtained from the GEO database as the training group data set.Random forest and lasso regression algorithms were used to screen the key genes of scleroderma and construct the ANN model for the diagnosis of scleroderma.The validation data sets GSE76807,GSE32413 and GSE59785 were used to verify the model,and the area under curve(AUC)analysis was used to evaluate the clinical application value of ANN model.The relative expression of key gene mRNA was verified by RT-qPCR experiment.The CIBERSORT algorithm was used to estimate the bioinformatics association between scleroderma and the screened biomarkers.Finally,the key genes were used to screen the targeted traditional Chinese medicine.Results A total of 167 differential genes were obtained.Furthermore,the five most relevant key genes(SERPINE2,SFRP4,SUGCT,FBLN5,NRXN2)were screened by machine learning,and the artificial neural network diagnosis model was constructed.The model was used to draw the subject operating characteristic(ROC)curves diagnosed by the training group and the verification group,and the AUC value of the training group was 1.000.The AUC of verification group were 0.770,0.795 and 0.872 respectively.The result of RT-qPCR experiment is consistent with that of machine learning algorithm.Immune cell infiltration analysis showed that the relative content of memory CD4+T cells was significantly increased in scleroderma group,while the relative content of γ δ T cells in normal group was significantly increased.Key genes are associated with macrophage M1,T cells,memory activated CD4+T cells,resting mast cells,CD8+T cells and so on.According to the key genes,12 traditional Chinese medicines were screened.Most of the four qi and five flavors belong to warm,cold,flat,sweet,pungent and bitter,and most of them belong to the meridians of liver,spleen and lung.Conclusion the artificial neural network diagnosis model of key genes of scleroderma is constructed,which can be used in clinical diagnosis of scleroderma,and the potential targeted traditional Chinese medicine for the treatment of scleroderma is predicted,which provides a new perspective for exploring the pathogenesis of scleroderma.

Objective To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.Methods The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes,and the differential genes were identified by Random forest,LASSO regression and SVM algorithms.Based on these differential genes,an artificial neural network model was constructed,and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807.The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma.The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers.Results A total of 24 differential genes were obtained,including 11 up-regulated and 13 down-regulated genes.Seven most relevant mitochondria-related genes(POLB,GSR,KRAS,NT5DC2,NOX4,IGF1,and TGM2)were screened using 3 machine learning algorithms,and the artificial neural network diagnostic model was constructed.The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis(0.740 for the verification dataset and 0.980 for cross-over validation).RT-qPCR detected significant up-regulation of POLB,GSR,KRAS,NOX4,IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma.Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells,immature B cells,resting dendritic cells,memory activated CD4+T cells,M0 macrophages,monocytes,resting memory CD4+T cells and mast cell activation.Conclusion The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.

Objective To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.Methods The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes,and the differential genes were identified by Random forest,LASSO regression and SVM algorithms.Based on these differential genes,an artificial neural network model was constructed,and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807.The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma.The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers.Results A total of 24 differential genes were obtained,including 11 up-regulated and 13 down-regulated genes.Seven most relevant mitochondria-related genes(POLB,GSR,KRAS,NT5DC2,NOX4,IGF1,and TGM2)were screened using 3 machine learning algorithms,and the artificial neural network diagnostic model was constructed.The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis(0.740 for the verification dataset and 0.980 for cross-over validation).RT-qPCR detected significant up-regulation of POLB,GSR,KRAS,NOX4,IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma.Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells,immature B cells,resting dendritic cells,memory activated CD4+T cells,M0 macrophages,monocytes,resting memory CD4+T cells and mast cell activation.Conclusion The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.

OBJECTIVE To study the effects of Wenyang huazhuo tongluo formula conta ined serum on the expression and methylation of retinoic acid related nuclear orphan receptor （RORγt），and to explore the mechanism of its regulation of Th 17 cell proliferation in the treatment of scleroderma. METHODS Wistar rats were given 15，30，60 g/（kg·d）Wenyang huazhuo tongluo formula intragastrically to prepare different doses of drug-contained serum ，and peripheral blood of scleroderma patients were collected to sort Th 17 cells. Using blank serum as blank control ，methyltransferase inhibitor decitabine （DCA）as positive control ， Th17 cells were treated with different concentrations of drug-contained serum ，and then the proliferation of Th 17 cells was detected by CCK- 8；mRNA expressions of RORγt and IL-17 were detected by qRT-PCR ，and the protein expression of RORγt was detected by Western blot assay ；the protein expression of IL- 17 in the cell supernatant was detected by ELISA ，and the methylation level of RORγt gene promoter was detected by methylation-specific PCR. The transcriptional activity of RORγt gene promoter was detected by dual-luciferase assay. RESULTS Compared with blank control ，Wenyang huazhuo tongluo formula contained serum and DCA could inhibit the proliferation of Th 17 cells（P＜0.05），reduced the mRNA and protein expressions of RORγt and IL-17，enhanced the methylation level of RORγt gene promoter and attenuated ， the transcription activity of RORγt gene promoter. Except for mRNA expression of Th 17 and the methylation level of RORγt gene promoter in drug-contained serum low-dose group ，there were statistical significance in above indexes of other groups（P＜0.05）. CONCLUSIONS Wenyang huazhuo tongluo formula can promote the methylation lev el of RORγt gene，and inhibit the expression of RORγt and the secretion of IL-17，so as to inhibit the proliferation of Th 17 cells.