The purpose of this study is to characterize the re-epithelialization of wound healing in canine prostatic urethra and to evaluate the effect of this re-epithelialization way after two-micron laser resection of the prostate (TmLRP). TmLRP and partial bladder neck mucosa were performed in 15 healthy adult male crossbred canines. Wound specimens were harvested at 3 days, and 1, 2, 3, and 4 weeks after operation, respectively. The histopathologic characteristics were observed by hematoxylin and eosin staining. The expression of cytokeratin 14 (CK14), CK5, CK18, synaptophysin (Syn), chromogranin A (CgA), uroplakin, transforming growth factor-β1(TGF-β1), and TGF-β type II receptor in prostatic urethra wound were examined by immunohistochemistry and real-time polymerase chain reaction, respectively. Van Gieson staining was performed to determine the expression of collagen fibers in prostatic urethra and bladder neck would. The results showed that the re-epithelialization of the prostatic urethra resulted from the mobilization of proliferating epithelial cells from residual prostate tissue under the wound. The proliferating cells expressed CK14, CK5, but not CK18, Syn, and CgA and re-epithelialize expressed uroplakin since 3 weeks. There were enhanced TGF-β1 and TGF-β type II receptor expression in proliferating cells and regenerated cells, which correlated with specific phases of re-epithelialization. Compared with the re-epithelialization of the bladder neck, re-epithelialization of canine prostatic urethra was faster, and the expression of collagen fibers was relatively low. In conclusion, re-epithelialization in canine prostatic urethra resulted from prostate basal cells after TmLRP and this re-epithelialization way may represent the ideal healing method from anatomic repair to functional recovery after injury.
Background: Personalized prediction of the risk of symptomatic intracerebral hemorrhage (sICH) after stroke thrombolysis is clinically useful. Machine-learning-based modeling may provide the personalized prediction of the risk of sICH after stroke thrombolysis. Methods: We identified 2578 thrombolysis-treated ischemic stroke patients between January 2013 and December 2016 from a multicenter database, where 70% were used to train models and the remaining 30% were used as the nominal test sets. Another 136 consecutive tissue plasminogen-activated-treated patients between January 2017 and December 2017 from our institute were enrolled as the independent test sets for clinical usability evaluation. Five machine-learning models were developed to predict the risk of sICH after stroke thrombolysis, and the receiving operating characteristic (ROC) was used to compare the prediction performance. Results: In total, 2237 cases were included in our study, of which 102 had sICH transformation (4.56%). Finally, the three-layer neuro network was selected with the best performance on nominal test sets (AUC = 0.82). The probability of the model score was further categorized into three risk ranks (18.97%, 5.63%, and 0.81%) according to the risk distribution. Implementing our system in clinical practice was associated with reduced computed tomography (CT)-to-treatment time (CTT; 41 min versus 52 min, p < 0.001). All sICH patients were correctly predicted to be within the high-sICH risk rank. Conclusions: The machine-learning-based modeling is feasible for providing personalized risk prediction of sICH after stroke thrombolysis, and is able to reduce the CTT. More data are needed to further optimize the model and improve the accuracy of prediction.
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