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The role of insulin-like growth factor I (IGF-I) in the growth and development of prostate cancer was studied using established human prostate cancer cell lines. Under steroid and growth factor-free culture conditions, IGF-I significantly stimulated the androgen-independent cell lines PC-3 and DU-145 to incorporate [3H]thymidine into DNA, while the androgen-dependent cell line, LNCaP, was not affected. However, in the presence of dihydrotestosterone (DHT), DNA synthesis of LNCaP cells was stimulated by IGF-I in a dose-dependent manner. None of the cell lines tested secreted an immunoreactive level of IGF-I into their conditioned medium. Characterization of receptors by ligand binding assays revealed that all prostate cancer cell lines tested express specific binding sites for IGF-I with similar dissociation constants (0.23-0.39 nM). Crosslinking studies supported the suggestion that 125I-IGF-I was bound to a receptor on these cells. The IGF-I receptor concentrations of androgen-independent cell lines were significantly higher than those of the androgen-dependent cell line. Androgen appeared to affect neither the expression of IGF-I receptors nor the secretion of IGF-I. The results suggest that IGF-I may play an important role in stimulating the growth and progression of prostate cancer.

Overfitting is a crucial problem in deep neural networks, even in the latest network architectures. In this paper, to relieve the overfitting effect of ResNet and its improvements (i.e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization. ShakeDrop is inspired by Shake-Shake, which is an effective regularization method, but can be applied to ResNeXt only. ShakeDrop is more effective than Shake-Shake and can be applied not only to ResNeXt but also ResNet, Wide ResNet, and PyramidNet. An important key is to achieve stability of training. Because effective regularization often causes unstable training, we introduce a training stabilizer, which is an unusual use of an existing regularizer. Through experiments under various conditions, we demonstrate the conditions under which ShakeDrop works well.

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