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Objectives: 5α-reductase inhibitor (5-ARI) is a commonly used medicine in the treatment of lower urinary tract symptoms (LUTS) associated with benign prostatic hyperplasia (BPH). Our study mainly focuses on the mechanism of BPH development after 5ARI treatment. Materials and Methods:Prostate specimens from patients were collected. Insulinlike growth factor 1 (IGF-1), Beclin-1, LC3 levels, was analysed by immunohistochemistry. The role IGF-1 on autophagic flux in prostate epithelial cells was studied.Additionally, effect of autophagy on recombinant grafts consisting of prostate stromal and epithelial cells in nude mice was investigated. Results:We demonstrated that IGF-1 expression is down-regulated in prostate fibroblasts after long-term 5-ARI application. A decrease in IGF-1 levels was found to activate autophagic flux through the mTOR pathway in prostate epithelial cells, while the inhibition of IGF-1 receptor function induced autophagy in prostate epithelial cells. In addition, we revealed that blocking autophagic flux initiation can reduce the volume of recombinant grafts in vivo. Finally, our findings suggest that long-term 5-ARI application reduces IGF-1 secretion by prostatic stromal cells, thereby inducing autophagy of prostatic epithelial cells, which is one of the mechanisms underlying BPH pathogenesis and progression. Conclusions:Focusing on the autophagy induced by low levels of IGF-1 in prostatic epithelial cells, after elucidating AR signalling impairment of prostate stromal cells, might provide a novel strategy for the treatment and prevention of BPH development. S U PP O RTI N G I N FO R M ATI O NAdditional supporting information may be found online in the Supporting Information section at the end of the article.How to cite this article: Yang B-Y, Jiang C-Y, Dai C-Y, et al. 5-ARI induces autophagy of prostate epithelial cells throughsuppressing IGF-1 expression in prostate fibroblasts. Cell

An evolutionary ensemble modeling (EEM) method is developed to improve the accuracy of warfarin dose prediction. In EEM, genetic programming (GP) evolves diverse base models, and genetic algorithm optimizes the parameters of the GP. The EEM model is assembled by using the prepared based models through a technique called "bagging." In the experiment, a dataset of 289 Chinese patients, which is provided by The First Affiliated Hospital of Soochow University, is used for training, validation, and testing. The EEM model with selected feature groups is benchmarked with four machine-learning methods and three conventional regression models. Results show that the EEM model with M2+G group, namely, age, height, weight, gender, CYP2C9, VKORC1, and amiodarone, presents the largest coefficients of determination (R2), highest percentage of predicted dose within 20% of the actual dose (20%-p), smallest mean absolute error (mae), mean squared error (mse), root-mse on the test set, and the least decrease in R2 from the training set to the test set. In conclusion, the EEM method with M2+G delivers superior performance and can therefore be a suitable prediction model of warfarin dose for clinical application.

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