Objective To study the preparation method of compound tissue-engineering scaffolds of the PLA/silk fibroin and evaluate its biological features.Methods The PLA scaffolds matrix were dipped into the silk fibroin solution,then dried,and PLA/silk fibroin scaffolds were prepared.There were two groups in the experiment,one group was PLA group,and the other one was compound scaffolds group.According to ISO-10993 standard,hematolysis test,dynamic coagulation time test,cell toxicity test,stimulation test and pyrogen test were performed in two groups,and the results were compared betwen two groups.Results In the stimulation test,the two kinds of materials had equally not aroused the obvious animal skin stimulation,it showed that the experiment was in accordance with the standard.In the pyrogen test,the two scaffolds material aroused the animal temperature rising without exception under 0.2℃ and the total number of degree was under 1.0℃,therefore there was no obvious difference between two groups.In the hematolysis test,the hemolysis rates of the two scaffolds samples were smaller than 5% equally(P=0.000),which indicated that the hemolysis of the compound scaffolds was better than that of the PLA scaffolds.In the dynamic coagulation time test,the coagulation time of the compound scaffolds(37 min) was longer than that of the PLA scaffolds(26 min).The anti-coagulation ability of the compound scaffolds was better than that of the PLA scaffdds.In the cell toxicity test,the cell growth situation of the compound scaffolds group was obviously better than that of the PLA group,and at the meantime the cell toxicity of the compound scaffolds was obviously smaller than that of the PLA scaffolds.Conclusion The material of PLA/silk fibroin compound scaffolds has the advanced biological consistent compared with the simplex scaffolds.Accordingly,the PLA/silk fibroin can be used as a scaffolds matrix to be transplanted into the body.

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.

OBJECTIVETo investigate the relationship between extranodal tumor deposits and prognosis in patients with colorectal cancer.

METHODSThe literatures on extranodal tumor deposits and postoperative survival rate in patients with colorectal cancer published at home and abroad from 1990 to 2014 were retrieved in 15 English literature databases such as MEDLINE/PubMed, Web of Science, Directory of Open Access Journals(DOAJ), SpringerLink and Chinese literature databases such as Chinese Biomedical Literature Database CD-ROM, China National Knowledge Infrastructure (CNKI) Database with the internet platform of Yonsei University Library. After screening for inclusion, data extraction and quality assessment, meta-analysis was conducted by the Review Manager 5.3 software.

RESULTSThere were 10 studies meeting the inclusion criteria for meta-analysis. The total sample size of the studies was 4 068 cases with ENTD(+) 727 cases, while ENTD(-) 3 341 cases. Meta analysis showed that 5-year overall survival rate and 5-year relapse-free survival rate were significantly lower in ENTD(+) group than those in ENTD(-) group (OR 0.27, 0.23; 95% CI:0.18 to 0.43, 0.16 to 0.34 respectively, both P=0.000); the 5-year overall survival rates were both significantly lower in ENTD(+) group as compared to ENTD(-) group for patients with N0 and N(+) colorectal cancer (both P<0.05).

CONCLUSIONExtranodal tumor deposits is a poor prognostic factor of patients with colorectal cancer.

Colorectal Neoplasms ; diagnosis ; pathology ; Disease-Free Survival ; Humans ; Neoplasm Recurrence, Local ; Prognosis ; Survival Rate