Background and Objectives: The search for a suitable alternative for urethral defect is a challenge in the field of urethral tissue engineering. Induced pluripotent stem cells (iPSCs) possess multipotential for differentiation. The in vitro derivation of urothelial cells from mouse-iPSCs (miPSCs) has thus far not been reported. The purpose of this study was to establish an efficient and robust differentiation protocol for the differentiation of miPSCs into urothelial cells. Methods: and Results: Our protocol made the visualization of differentiation processes of a 2-step approach possible. We firstly induced miPSCs into posterior definitive endoderm (DE) with glycogen synthase kinase-3β (GSK3β) inhibitor and Activin A. We investigated the optimal conditions for DE differentiation with GSK3β inhibitor treatment by varying the treatment time and concentration. Differentiation into urothelial cells, was directed with all-trans retinoic acid (ATRA) and recombinant mouse fibroblast growth factor-10 (FGF-10). Specific markers expressed at each stage of differentiation were validated by flow cytometry, quantitative real-time polymerase chain reaction (qRT-PCR) assay, immunofluorescence staining, and western blotting Assay. The miPSC-derived urothelial cells were successfully in expressed urothelial cell marker genes, proteins, and normal microscopic architecture. Conclusions We built a model of directed differentiation of miPSCs into urothelial cells, which may provide the evi-dence for a regenerative potential of miPSCs in preclinical animal studies.

Objective To construct a prediction model for post-neoadjuvant therapy recurrent laryngeal nerve lymph node(RLN LN)status via clinical and CT image data in esophageal cancer patients pre-neoadjuvant therapy.Methods A retrospective analysis was conducted on 403 patients with locally advanced esophageal cancer who received neoadjuvant therapy and radical resection for esophageal cancer.All patients were divided into a training cohort(n=270)and a validation cohort(n=133)randomly according to a 2:1 ratio.Clinical and imaging features associated with positive RLN LN pathology were selected by univariate analysis.Multivariate logistic stepwise regression model was used to construct the prediction model.The prediction ability of the model was evaluated by receiver operating characteristic(ROC)curve.Results The basic model included neoadjuvant therapy and RLN LN short diameter,with an area under the curve(AUC)of 0.7(training cohort)and 0.65(validation cohort).The final prediction model included neoadjuvant therapy,human albumin,platelet count,largest lymph node enhancement characteristics,whether the largest lymph node was in the recurrent laryngeal region,and RLN LN short diameter,with AUC of 0.83[95％confidence interval(CI)0.768-0.899]and 0.76(95％CI 0.645-0.887)for the training and validation cohorts,respectively.Conclusion The model based on clinical data and imaging features pre-neoadjuvant therapy for esophageal cancer can assist in clinically predicting the post-neoadjuvant therapy RLN LN status.