AIM: To improve the quantitive method of sarsasapogin in Kangbindu Tablet(Rhizoma Anemarrhenae, Fructus Forsythiae, etc.) METHODS: Applying HPLC-ELSD to replace TLCS. RESULTS: The new method was better than the old one in precise and repeat. CONCLUSION: The new method HPLC-ELSD is more useful.

Objective To investigate the expression of nuclear factor-κB (NF-κB) and p53 up-regulated modulator of apoptosis (PUMA) in acute lung injury (ALI) induced by severe acute pancreatitis (SAP), and the therapeutic role of proline dithiocarbamate (PDTC). Method SD rats weighed 200～ 250 g were randomly(random number) divided into sham operation group (A group, n = 18), ALI group (B group, n = 18) and PDTC treatment group (C group, n = 18). The model of SAP was eastablished by injecting 1 mL/kg of sodium tauarocholate into the pancreatic capsule of the rats in B group and C group. The model rats in C group were treated with PDTC one hour after modeling. Six rats of each group were sacrificed 6 h,12 h, and 24 hours after modeling. The histopathological changes in lung and pancreas were observed. The levels of NF-κB p65 and PUMA in lung were detected by using Western blotting, and the expressions of bcl-2, bax and caspase-3 mRNA in the lung were detected by using RT-PCR. The lung tissue was taken for examination under transmission electron microscope. TUNEL was used for detection of apoptotic alveolar epithelial cells. Results Six to 24 hours after modeling, the pathological scores in lung of ALI group were significantly higher than those of control group and PDTC group after sodium taurocholate injection ( P ＜ 0.05). The levels of NF-κB p65 and PUMA, and the expressions of bax and caspase3 mRNA in ALI group at different intervals were higher than those in control group and PDTC group ( P ＜ 0.05),whereas the expression of bcl-2 mRNA in ALI group was lower than that in control group and PDTC group ( P ＜0.05). The NF-κB p65 was correlated closely and positively with PUMA ( r= 0.987, P ＜ 0.01). Higher activity of caspase-3 acrtive units was seen in ALI group than that in control group and PDTC group ( P ＜ 0.05). The microvilli disappeared in ALI group 24 hours later. The apoptosis index in ALI group was higher than that in control group and PDTC group ( P ＜ 0.05). Conclusions The apoptosis of alveolar epithelial cells of rats in ALI group is caused by PUMA activated by NF-κB. PDTC treatment can inhibit apoptosis of alveolar epithelial cells of rats in ALI group by inhibiting the activation of NF-κB.

Objective:To explore the feasibility of using faster regional convolutional neural network (Faster R-CNN) to evaluate the status of circumferential resection margin (CRM) of rectal cancer in the magnetic resonance imaging (MRI).Methods:This study was registered in the Chinese Clinical Trial Registry (ChiCTR-1800017410). Case inclusion criteria: (1) the positive area of CRM was located between the plane of the levator ani, anal canal and peritoneal reflection; (2) rectal malignancy was confirmed by electronic colonoscopy and histopathological examination; (3) positive CRM was confirmed by postoperative pathology or preoperative high-resolution MRI. Exclusion criteria: patients after neoadjuvant therapy, recurrent cancer after surgery, poor quality images, giant tumor with extensive necrosis and tissue degeneration, and rectal tissue construction changes in previous pelvic surgery. According to the above criteria, MRI plain scan images of 350 patients with rectal cancer and positive CRM in The Affiliated Hospital of Qingdao University from July 2016 to June 2019 were collected. The patients were classified by gender and tumor position, and randomly assigned to the training group (300 cases) and the validation group (50 cases) at a ratio of 6:1 by computer random number method. The CRM positive region was identified on the T2WI image using the LabelImg software. The identified training group images were used to iteratively train and optimize parameters of the Faster R-CNN model until the network converged to obtain the best deep learning model. The test set data were used to evaluate the recognition performance of the artificial intelligence platform. The selected indicators included accuracy, sensitivity, positive predictive value, receiver operating characteristic (ROC) curves, areas under the ROC curves (AUC), and the time taken to identify a single image.Results:The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CRM status determined by the trained Faster R-CNN artificial intelligence approach were 0.884, 0.857, 0.898, 0.807, and 0.926, respectively; the AUC was 0.934 (95% CI: 91.3% to 95.4%). The Faster R-CNN model's automatic recognition time for a single image was 0.2 s.Conclusion:The artificial intelligence model based on Faster R-CNN for the identification and segmentation of CRM-positive MRI images of rectal cancer is established, which can complete the risk assessment of CRM-positive areas caused by in-situ tumor invasion and has the application value of preliminary screening.

Objective:To explore the feasibility of using faster regional convolutional neural network (Faster R-CNN) to evaluate the status of circumferential resection margin (CRM) of rectal cancer in the magnetic resonance imaging (MRI).Methods:This study was registered in the Chinese Clinical Trial Registry (ChiCTR-1800017410). Case inclusion criteria: (1) the positive area of CRM was located between the plane of the levator ani, anal canal and peritoneal reflection; (2) rectal malignancy was confirmed by electronic colonoscopy and histopathological examination; (3) positive CRM was confirmed by postoperative pathology or preoperative high-resolution MRI. Exclusion criteria: patients after neoadjuvant therapy, recurrent cancer after surgery, poor quality images, giant tumor with extensive necrosis and tissue degeneration, and rectal tissue construction changes in previous pelvic surgery. According to the above criteria, MRI plain scan images of 350 patients with rectal cancer and positive CRM in The Affiliated Hospital of Qingdao University from July 2016 to June 2019 were collected. The patients were classified by gender and tumor position, and randomly assigned to the training group (300 cases) and the validation group (50 cases) at a ratio of 6:1 by computer random number method. The CRM positive region was identified on the T2WI image using the LabelImg software. The identified training group images were used to iteratively train and optimize parameters of the Faster R-CNN model until the network converged to obtain the best deep learning model. The test set data were used to evaluate the recognition performance of the artificial intelligence platform. The selected indicators included accuracy, sensitivity, positive predictive value, receiver operating characteristic (ROC) curves, areas under the ROC curves (AUC), and the time taken to identify a single image.Results:The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the CRM status determined by the trained Faster R-CNN artificial intelligence approach were 0.884, 0.857, 0.898, 0.807, and 0.926, respectively; the AUC was 0.934 (95% CI: 91.3% to 95.4%). The Faster R-CNN model's automatic recognition time for a single image was 0.2 s.Conclusion:The artificial intelligence model based on Faster R-CNN for the identification and segmentation of CRM-positive MRI images of rectal cancer is established, which can complete the risk assessment of CRM-positive areas caused by in-situ tumor invasion and has the application value of preliminary screening.