OBJECTIVE:To establish fingerprint of Zhizi jinhua pills(ZZJHW)and analyze the relationship of it with in vitro antioxidant activity,in order to provide the basis for the quality control of them. METHODS:HPLC method was adopted. The sep-aration was performed on a Sinochrom ODS-BP C18(200 mm×4.6 mm,5 μm)column with mobile phase consisted of 0.2% acetic acid(containing 3 mmol/L sodium heptanesulfonate solution)-acetonitrile(gradient elution)at the detection wavelength of 254 nm and flow rate of 0.8 ml/min. The column temperature was controlled at 38 ℃,and injection volume was 10 μl. The“Chromato-graphic Fingerprint Similarity Evaluation System for TCM”(2012.130723 edition) issued by Chinese Pharmacopoeia Commission was used to evaluate the similarity of the 12 batches of ZZJHW using baicalin as reference peak so as to attribute the common peak of fingerprint. DPPH free radical scavenging assay was used to investigate the in vitro antioxidant activity of 12 batches of ZZJHW,and the relationship between its fingerprint and antioxidant activity was studied. RESULTS:The fingerprint of 12 batches of ZZJHW was established and the similarity between the fingerprint of ZZJHW with their reference fingerprint were all above 0.9 (except S1,S2,S3,S12). 30 common peaks were marked,all of which were assigned to the herbs. Antioxidant experiment result showed the differences in the antioxidant capacity among different batches of ZZJHW;spectrum effect relationship showed that 13 common peaks were positively related with oxidation activity and 17 common peaks negatively related with it;among known com-ponents,oxidation activity components were mainly from Lonicera japonica,Scutellaria baicalensis and Rheum palmatum. CON-CLUSIONS:The spectrum effect relationship of established fingerprint with its antioxidant activity can provide reference for the quality control of ZZJHW.

DEDD is a member of the death-effector domain protein family. DEDD inhibits the Smad3 mediated transcriptional activity and participates in the regulation of apoptosis. In this study, how the death-effector domain of DEDD participates in the regulation of Smad3 activity and apoptosis has been further investigated. Immunoblotting, immunofluorescence and immunoprecipitation had been used to detect the effects of the full length DEDD and its two truncated mutants, N-DEDD and C-DEDD on Smad3 subcellular distribution, phosphorylation, and interaction between Smad4. The effects of the full length DEDD and its two truncated mutants on cell apoptosis and proliferation had also been explored by flow cytometry and MTT assay. It showed that DEDD and N-DEDD inhibit TGF-beta1 induced Smad3 nuclear translocation and the formation of Smad3-Samd4 complex. DEDD and its two mutants can induce cell apoptosis and inhibit cell proliferation. These results suggested that DEDD inhibits the activity of Smad3 through its death-effector domain. Both the two truncated mutants of DEDD participate in the regulation of apoptosis and cell proliferation.

BACKGROUND: Distinguishing between primary clear cell carcinoma of the liver (PCCCL) and common hepatocellular carcinoma (CHCC) through traditional inspection methods before the operation is difficult. This study aimed to establish a Faster region-based convolutional neural network (RCNN) model for the accurate differential diagnosis of PCCCL and CHCC. METHODS: In this study, we collected the data of 62 patients with PCCCL and 1079 patients with CHCC in Beijing YouAn Hospital from June 2012 to May 2020. A total of 109 patients with CHCC and 42 patients with PCCCL were randomly divided into the training validation set and the test set in a ratio of 4:1.The Faster RCNN was used for deep learning of patients' data in the training validation set, and established a convolutional neural network model to distinguish PCCCL and CHCC. The accuracy, average precision, and the recall of the model for diagnosing PCCCL and CHCC were used to evaluate the detection performance of the Faster RCNN algorithm. RESULTS: A total of 4392 images of 121 patients (1032 images of 33 patients with PCCCL and 3360 images of 88 patients with CHCC) were uesd in test set for deep learning and establishing the model, and 1072 images of 30 patients (320 images of nine patients with PCCCL and 752 images of 21 patients with CHCC) were used to test the model. The accuracy of the model for accurately diagnosing PCCCL and CHCC was 0.962 (95% confidence interval [CI]: 0.931-0.992). The average precision of the model for diagnosing PCCCL was 0.908 (95% CI: 0.823-0.993) and that for diagnosing CHCC was 0.907 (95% CI: 0.823-0.993). The recall of the model for diagnosing PCCCL was 0.951 (95% CI: 0.916-0.985) and that for diagnosing CHCC was 0.960 (95% CI: 0.854-0.962). The time to make a diagnosis using the model took an average of 4 s for each patient. CONCLUSION The Faster RCNN model can accurately distinguish PCCCL and CHCC. This model could be important for clinicians to make appropriate treatment plans for patients with PCCCL or CHCC.
Humans ; Liver Neoplasms/pathology* ; Retrospective Studies ; Carcinoma, Hepatocellular/pathology* ; Neural Networks, Computer