半夏白术天麻汤治疗眩晕高血压病举隅

高血压病是我国发病率较高的疾病之一,西医治疗以长期服用降压药物为主要治疗方式,但常因依从性差的原因导致血压控制不佳,而用中医辨证治疗对于首次发现高血压的患者,可明显有效地降低高血压的发病率。高血压病在中医学中属"眩晕",在临床中常常会用半夏白术天麻汤来治疗眩晕,本文通过跟师医案及临床收集介绍半夏白术天麻汤治疗高血压眩晕临证方药的运用。     

胸膝卧位在产程中矫正枕位异常的应用

目的:探讨胸膝卧位矫正枕位异常的可行性。方法:将单胎初产临产的枕位异常的产妇186例,随机分成两组,入选者均无绝对头盆不称、胎儿窘迫、严重妊娠并发症及合并症,其中96例为观察组采取胸膝卧位矫正枕位,另90例为对照组按常规处理。结果:观察组产程明显缩短,阴道分娩率明显增加,而胎儿窘迫及新生儿窒息率明显降低,两组比较有统计学意义。结论:胸膝卧位在产程中矫正枕位异常是一种切实可行的方法     

江苏省儿童专科夜间门诊诊察费定价政策成效分析

2019年5月20日,江苏省医保局推出调整了儿童专科夜间门诊诊察费收费标准举措。文章以江苏省某三甲综合医院为样本,围绕上述政策实施后儿科诊疗工作量、病人满意度、医院收入影响等方面进行了调查,结果表明,上述政策可满足儿科病人就诊差异化需求,合理分流病人,保障急诊优先的诊疗秩序。     

Subscapularis Transthoracic Versus Posterolateral Approaches in the Surgical Management of Upper Thoracic Tuberculosis: A Prospective,Randomized Controlled Study

The objective of the present study was to evaluate the clinical, radiological, and functional outcomes of a subscapularis transthoracic surgical approach and a posterolateral surgical approach with debridement, bone graft fusion, and internal fixation for the treatment of upper thoracic tuberculosis.There is currently debate over the best surgical approach for the treatment of upper thoracic tuberculosis. Traditionally, the subscapularis transthoracic approach has been preferred; however, the posterolateral approach has gained popularity in the past few years.A prospective, consecutive cohort of 43 upper thoracic tuberculosis patients with a mean age of 39 years (range: 20–52 years) was followed up for a minimum of 12 months (range: 12–60 months). Patients were randomly divided into 2 groups. Group A (n = 21) was treated by the subscapularis transthoracic approach and group B (n = 22) was treated by the posterolateral approach. All cases were evaluated for clinical, radiological, and functional outcomes. Intraoperative blood loss, operative duration, intraoperative and postoperative complications, hospital stay, the cure rate, fusion time, and the Frankel scale were used for clinical and functional evaluation, whereas the kyphosis angle was used for radiological evaluation.Grafted bones were fused by 10 months in all cases. There was no statistically significant difference between groups before surgery in terms of gender, age, segmental tuberculosis, erythrocyte sedimentation rate (ESR), Frankel scale, or Cobb''s angle (P > 0.05). The average operative duration for Group B was lower than that of Group A. There were no significant differences in intraoperative blood loss, intraoperative and postoperative complications, hospital stay, grafted bone fusion time, or cure rate between groups (P > 0.05). The Cobb''s angle correction rate for group B (68.5%) was significantly better than that of group A (30.9%). The neurological score showed significant postoperative improvement in both groups, with no significant difference between the groups.The subscapularis transthoracic approach and the posterolateral approach with debridement, bone graft fusion, and internal fixation are both sufficient and satisfactory for the surgical treatment of upper thoracic tuberculosis. However, the posterolateral approach is superior to the subscapularis transthoracic approach in terms of surgical trauma, operative time, and kyphosis correction.     

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.