Objective To explore the effect of endovascular treatment on Stanford type B aortic dissection. Methods The clinical data of 12 cases of Stanford type B aortic dissection were analysed retrospectively. Results All the cases were male, the age ranged from 40-68 years with a mean of 52.1 years. Among the 12cases, 10 patients underwent endoluminal treatment, the instant technique was successfully performed in 10 patients. Endoleak happened in 1 case because of the stent-graft deployment, but automatically stopped 3 days later. In other 9 patients, Angiography after the operation showed that all the rupture areas were sealed completely, and the celiac arteries blood supply were recovered via the true lumen and no blood stream was shown in the false cavity. 2 patients received conservative treatment died, one died of failure of respiration and another died of rupture of the dissection. Conclusions In the treatment of Stanford B type aortic dissection, if the selection of the patient is correct, endoluminal technique is much simple, safe, less trauma, and less complications as compared to the traditional operation, and it can also shorten the hospital stay of the patient. Conservative treatment can not control the development of the diseases, and easily results in death of the patient.

Objective:Semaphorin 4D (Sema4D) acts as a regulator for axon guidance in central nervous system development. However, new evidence indicates that Sema4D has a previously unrecognized function, namely, compensatory angiogenic factor. This study aimed to investigate the effect of Sema4D on tumor growth and vascularity of colorectal carcinoma (CRC) in nude mice. Meth-ods:Sema4D was knocked down in CRC cells by infecting the cells with lentiviruses coding for Sema4D shRNA. Two groups of cells, namely, those infected with control viruses and those infected with Sema4D shRNA viruses, were subjected to migration assay to test their ability induce endothelial cell migration. The two cell groups were subcutaneously injected into nude mice. Tumor growth was documented, and the tumors harvested from the mice were subjected to immunohistochemistry or immuno fl uorescence analyses. Re-sults:In vitro migration assay results indicated that media conditioned by HCT-116 cells infected with Sema4D shRNA lentiviruses in-duced low endothelial cell migration. The two groups of subcutaneously inoculated cells showed 100%tumorigenicity. However, tumor growth rates were significantly different between the two groups. Xenografts in which Sema4D was downregulated showed marked re-duction in tumor size and vascularity. Conclusion:Cancer cells may highly express Sema4D to trigger net neo-angiogenesis and gener-ate a tumor blood supply system. Thus, Sema4D could potentially be a target in anti-angiogenic therapy of CRC patients.

Objective To construct a nomogram prognostic model for predicting the survival of patients with lung adenocarcinoma based on the large sample data from the SEER database. Methods We retrospectively analyzed the clinical data of patients who were diagnosed with lung adenocarcinoma from 2010 to 2015 in the SEER database. A nomogram model was created based on independent parameters influencing the prognosis of patients with lung adenocarcinoma using Lasso Cox regression analysis. The C-index and calibration curve were utilized to assess the ability to distinguish and calibrate the nomogram. NRI and DCA curves were used to evaluate the prediction ability and net benefit of the nomogram. Results A total of 15 independent risk factors affecting the prognosis of lung adenocarcinoma were identified and integrated into the nomogram model. The C-index of the prediction model was 0.819 in the training cohort and 0.810 in the validation cohort. The predicted specific survival rate of the 1-, 3- and 5-year calibration curves of the training cohort and the validation cohort were consistent with the actual specific survival rate. In comparison to the 7th edition of the AJCC TNM staging system, the NRI and DCA curves demonstrated a considerable boost to the predictive capacity and net benefits achieved by the nomogram model. The risk stratification model constructed with this nomogram model was able to distinguish the patients with different risks well (P < 0.0001). Conclusion A nomogram prognostic model is successfully developed and validated, which provides a simple and reliable tool for the survival prediction of the patients with lung adenocarcinoma. Meanwhile, the risk stratification model constructed by the prediction model can conveniently screen patients with different risks, which is important for the individualized treatment of lung adenocarcinoma patients.

Objective:To investigate the influencing factors for celiac lymph node metastasis in thoracic esophageal squamous cell carcinoma (TE-SCC), construct a prediction model of celiac lymph node metastasis in TE-SCC, and stratify the probability of celiac lymph node metastasis.Methods:The retrospective case-control study was conducted. The clinicopathological data of 443 patients with TE-SCC who underwent thoracoscopic and laparoscopic esophagectomy with systematic lymph node dissection in the First Affiliated Hospital of Zhengzhou University between March 2015 and April 2019 were collected. There were 259 males and 184 females, aged from 41 to 81 years, with a median age of 64 years. The nomogram prediction model was constructed based on the results of multivariate analysis of influencing factors for celiac lymph node metastasis in TE-SCC, of which calibration curve and decision curve were drawed. The predictive performance was evaluated using the concordance index. The score for celiac lymph node metastasis in TE-SCC predicted by nomogram model was used for further recursive partitioning analysis, and patients were stratified into risk subgroups using the decision-making tree model. Observation indicators: (1) celiac lymph node metastasis in TE-SCC; (2) analysis of influencing factors for celiac lymph node metastasis in TE-SCC; (3) construction of nomogram prediction model of celiac lymph node metastasis in TE-SCC; (4) construction of decision-making tree model of celiac lymph node metastasis in TE-SCC and risk subgroup analysis of celiac lymph node metastasis probability. Measurement data with skewed distribution were represented as M (range). Count data were represented as absolute numbers and percentages, and comparison between groups was analyzed using the chi-square test. Comparison of ordinal data between groups was analyzed using the nonparametric rank sum test. Multivariate analysis was performed using the Logistic regression model. Based on Logistic regression model multivariate analysis, a new nomogram model was constructed using the RStudio 3.4 software. Results:(1) Celiac lymph node metastasis in TE-SCC: celiac lymph node metastasis was found in 89 of the 443 patients, with a celiac lymph node metastasis rate of 20.09%(89/443). (2) Analysis of influencing factors for celiac lymph node metastasis in TE-SCC. Results of univariate analysis showed that tumor location, tumor length, tumor differentiation degree, pathological T staging, nerve invasion, vessel invasion, and thoracic lymph node metastasis were related factors for celiac lymph node metastasis in TE-SCC ( χ2=12.177, Z=-2.754, -4.218, -4.254, χ2=3.908, 33.025, 30.387, P<0.05). Results of multivariate analysis showed that tumor location, vessel invasion, and thoracic lymph node metastasis were independent influencing factors for celiac lymph node metastasis in TE-SCC ( odds ratio=2.165, 3.442, 2.876, 95% confidence interval: 1.380-3.396, 1.787-6.633, 1.631-5.071, P<0.05). (3) Construction of nomogram prediction model of celiac lymph node metastasis in TE-SCC: based on the factors screened by multivariate analysis, including tumor location, vessel invasion, and thoracic lymph node metastasis, the nomogram prediction model of celiac lymph node metastasis in TE-SCC was established, with the concordance index of 0.846. The calibration curve showed a high consistency between the celiac lymph node metastasis probability estimated by the prediction model and the actual rate of celiac lymph node metastasis. The decision curve showed that the nomogram prediction model of celiac lymph node metastasis in TE-SCC had a good prediction value when the probability threshold was 0.001-0.819.(4) Construction of decision-making tree model of celiac lymph node metastasis in TE-SCC and risk subgroup analysis of celiac lymph node metastasis probability: patients were stratified into six risk subgroups using the decision-making tree model based on the celiac lymph node metastasis probability. The group A included patients with no vessel invasion+negative thoracic lymph node, group B included patients with no vessel invasion+the number of positive thoracic lymph nodes of 1-3, group C included patients with no vessel invasion+the number of positive thoracic lymph nodes of ≥4, group D included patients with vessel invasion+the number of positive thoracic lymph nodes of 0-2+upper or middle thoracic esophageal carcinoma, group E included patients with vessel invasion+the number of positive thoracic lymph nodes of 0-2+lower thoracic esophageal carcinoma, group F included patients with vessel invasion+the number of positive thoracic lymph nodes of ≥3. The group A was low-risk group with the celiac lymph node metastasis probability of 11%, group B and D were intermediate low-risk groups with the celiac lymph node metastasis probability of 27% and 21%, group C and E were the intermediate high-risk groups with the celiac lymph node metastasis probability of 56% and 55%, and group F was high-risk group with the celiac lymph node metastasis probability of 80%. Conclusions:The tumor location, vessel invasion, and thoracic lymph node metastasis are independent influencing factors for celiac lymph node metastasis in TE-SCC. Vessel invasion has the dominant influence on celiac lymph node metastasis, followed by the number of positive thoracic lymph nodes, and then the tumor location. Patients can be stratified into six risk subgroups based on the nomogram prediction model and decision-making tree model of celiac lymph node metastasis in TE-SCC.