The nervous system governs both ontogeny and oncology. Foundational discoveries have clarified the direct communication of neurotransmitters with tumors and indirect interactions through neural effects on the immune system and the tumor microenvironment. Meantime, the nervous system is susceptible to cancer and its treatment. Chemotherapy-induced peripheral neuropathy (CIPN) is the most common side effects that significantly reduce the efficacy of anti-cancer treatment and patients' quality of life by leading to dose reduction or early cessation of chemotherapy. However, there are no effective strategies to reverse or treat CIPN. A better understanding of the mechanisms is expected to enable the development of the next generation of therapies. Here, we summarize the recent important studies on clinical manifestations, risk factors, prediction, pathogenesis, prevention, and treatment of CIPN. We also provide perspectives and insights regarding the rationales of bidirectional interactions between cancer and the nervous system.

The problem of estimating a function’s derivative arises in many scientific settings, from medical imaging to astronomy. In this paper, we consider a hard thresholding wavelet approach to certain blind deconvolution problem based on the heteroscedastic data. Motivated by Delaigle & Meister’s work, the adaptive wavelet estimators are proposed and their asymptotic properties are investigated. It is shown that the derivative estimators for the blind deconvolution model are spatially adaptive and attain the optimal rate of convergence up to a logarithmic factor over a range of Besov classes. Our theorems generalize the results of Cai and Navarro et al. in some sense.