Development of a Nomogram Based on Clinicopathological and Biological Features to Predict Neck Lymph Node Metastasis in Hypopharyngeal Squamous Cell Carcinoma

Background: Clinicopathological and biological features are associated with neck lymph node metastasis (LNM) of hypopharyngeal squamous cell carcinoma (HSCC). However, there is no complete nomogram combining multiple factors that can be used to accurately predict the neck LNM status for HSCC patients. Purpose: To guide the selection of surgical methods and radiotherapy areas for hypopharyngeal cancer. In this study, a nomogram was developed to combine these risk factors to predict neck LNM and guide the treatment of HSCC. Material and Methods: This retrospective study included 117 patients (training cohort, 64 patients;trial cohort, 53 patients). Biological characteristics of HSCC patients were assessed using immunohistochemical staining, and data of patient age, gender, and preoperative computed tomography (CT) scan reports were collected. Significant risk factors in univariate analysis were further identified to be independent variables in multivariate logistic regression analysis, which were then incorporated in and presented with a nomogram by using the rms package in R software. Receiver operating characteristic (ROC) curves and calibration curves were used to validate the discrimination and accuracy in the training and validation cohorts, respectively, and clinical usefulness was verified in decision curve analysis curves. Results: All variables with P-values < 0.2 in the univariate analysis were selected for multivariate logistic regression analysis to further identify independent risk factors for neck LNM. In multivariate logistic regression analysis, variables with P-values < 0.2 were identified as independent risk factors and then used to construct the nomogram. In total, five independent predictors, including the maximum tumor diameter in CT, tumor cell differentiation, LNM status in CT, Stathmin1 expression level, and lymphatic vessel invasion were included in the nomogram. The area under the ROC curve (AUC) was 0.916 (95% confidence interval [CI], 0.833 - 1.000) and AUC of 0.928 (95% CI, 0.864 - 1.000) in internal validation and the external validation. Conclusions: Both the internal validation in the training cohort and the external validation in the validation cohort showed that the nomogram had good discrimination, accuracy, and excellent clinical usefulness. The nomogram based on clinicopathological and biological features developed in this study has strong predictive power and could be used to predict neck LNM of HSCC in clinical practice.

Gypenoside, the Main Active Compound of Gynostemma pentaphyllum , Mitigates the Diabetic Nephropathy through Down-regulating mTOR

Background:Diabetic nephropathy(DN),as a complication of diabetes,is featured with hypertension,hyper-glycemia,proteinuria and edema.Gypenoside(GP),the main active compound of Gynostemma pentaphyllum,is proved to be effective for DN.In our previous research,we found that GP could protect the glomerulus and re-duce proteinuria by up-regulating the expression of nestin and down-regulating TGFB1.However,the panoramic mechanism of GP against DN is still unclear.Objective:This research is designed to reveal the mechanism of GP on DN through network pharmacology and in vivo and in vitro experimental verification.Methods:In this study,active compounds and targets of Gynostemma pentaphyllum were collected from TCMSP.DisGeNET was used for obtaining the targets of DN.The protein-protein interaction network was acquired from the STRING database and analyzed by the MCODE plugin.GO and KEGG enrichment analysis were constructed to explore further information.In vivo and in vitro experiments were also carried out to evaluate the reliability of this study.Western blotting and RT-PCR were used to detect mTOR,4E-BP1,p70s6k protein expression and Mtor mRNA expression in DN rats,respectively.AKT1,TP53,ESR1 and PTEN protein expression in MPC-5 cells were detected by Western blotting.Results:Twenty-four compounds and 217 targets were selected from Gynostemma pentaphyllum,of which 36 targets overlapped with DN were taken for the potential targets.The results showed that Quercetin,Rhamnazin,Isofucosterol and 3′-methyleriodictyol corresponded to more targets,AKT1,TP53,MYC,ESR1,PTEN were more active.36 potential targets were mainly involved in autoimmunity,inflammatory response,metabolism and autophagy.In vivo and in vitro experiments showed that GP might protect the podocytes of DN rats by decreasing the protein expression of mTOR,4EBP1,p70s6k,as well as the mRNA expression of Mtor,and it had the function in regulating the potential targets through decreasing the protein expression of AKT1,TP53 and ESR1 and increasing the expression of PTEN.Conclusion:This research demonstrates that various compounds of Gynostemma pentaphyllum may intervenes in DN through targets of multiple signaling pathways,which involves a large number of biological processes.It can provide novel insights for further research of the mechanism of GP in the treatment of DN.

Load analyzing of nonstationary load history of engineering vehicles by switching Markov chain

Engineering vehicles are widely used under various harsh working conditions.For many components in them,service loadings they suffered are usually random and nonstationary due to their remarkable characteristic called cyclic operation.To deal with that,section method can be applied.However,this method will neglect those transition cycles caused by switching load section,which can contribute a lot to fatigue.In order to consider those transition cycles,this paper applied the model called“Switching Markov Chain of Turning Points”(SMCTP).Then the expected rain-flow matrix is compared with the overall rain-flow matrix conducted by section method.The comparison result shows that SMCTP can perform well in processing nonstationary loadings.As a result,the Switching Markov Chain method(SMC)was proved to be effective in stochastically characterizing the nonstationary switching loadings of engineering vehicles.