腮腺切除术中沿面神经总干顺行解剖面神经的效果及安全性

目的探讨腮腺切除术中应用沿面神经总干顺行解剖面神经的治疗效果及安全性。方法选取我院2016年2月至2019年3月收治的60例择期行腮腺切除术的患者,随机分为观察组与对照组各30例。对照组沿面神经总干逆行解剖面神经,观察组沿面神经总干顺行解剖面神经,观察两组患者术后1个月面神经功能及术后并发症发生情况。结果术后1个月,两组患者的面神经功能分级比较无统计学差异(P>0.05);观察组的术后并发症发生率为10.00%,明显低于对照组的33.33%(P <0.05)。结论临床行腮腺切除术中,沿面神经总干顺行解剖面神经可降低术后并发症发生率,值得推广。     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.     

WT1基因与急性髓系白血病的预后及主动特异性免疫治疗的研究进展

WT1基因位于人类染色体11p13，在80%的急性髓系白血病（acute myeloid leukemia，AML）患者中高表达，是AML预后不良的分子标志，可作为AML预后评估和微小残留病变（minimal residual disease，MRD）监测的有效指标。由于WT1在AML中均有异常高表达，故认为是一种AML抗原，可作为特异性免疫治疗的新靶点。一些小规模的临床试验证实以WT1为靶点的免疫治疗是有效的、安全的，这些免疫治疗可作为那些有高危复发风险及初始标准化疗失败的AML患者的辅助治疗。本文就近几年WT1与AML的预后及有关以WT1为靶点的主动特异性免疫治疗的研究进展予以综述。