郑州地区孕妇铁营养状况及其影响因素调查

目的 探讨郑州地区孕妇铁营养状况及其影响因素。方法 以2021年4—8月在郑州市某三家医院建立健康档案并进行孕期体检的孕妇作为研究对象进行问卷调查及铁营养状况检测,采用描述流行病学方法对孕妇铁营养状态进行分析,并采用单、多因素分析方法对孕妇铁缺乏状况影响因素进行分析。结果 共有3 258例孕妇参与本次研究,其中妊娠早期989例,占30.36%,妊娠中期1 125例,占34.53%,妊娠晚期1 144例,占35.11%。在3 258名孕妇中,共有773例为铁缺乏,铁缺乏的检出率为23.73%。多因素分析结果显示,年龄≥35岁（OR=2.726）、处于妊娠的中期或晚期（OR=3.037,3.438）、月经初潮年龄<13岁（OR=2.432）、经产妇（OR=3.493）、流产次数≥3次（OR=2.648）、孕前月经量≥100 mL/次（OR=2.420）、文化程度为初中及以上（OR=0.517,0.459）、居住在农村（OR=3.684）、家庭月收入>3 000元及以上（OR=0.601,0.542）、居住房屋装修时间<1年（OR=6.449）、罹患消化系统疾病（OR=4.716）、有营养补齐剂摄入（OR=0.355）是孕妇铁缺乏的影响因素。结论 郑州市孕妇铁缺乏检出率较高,需要尽早给予针对性的干预。部分因素被确定为该地区孕妇人群发生铁元素缺乏的影响因素,可选择性的对其中危险因素给予干预。     

Mixed Reality Combined with Three‐Dimensional Printing Technology in Total Hip Arthroplasty: An Updated Review with a Preliminary Case Presentation

Three‐dimensional (3D) printing technology, virtual reality, and augmented reality technology have been used to help surgeons to complete complex total hip arthroplasty, while their respective shortcomings limit their further application. With the development of technology, mixed reality (MR) technology has been applied to improve the success rate of complicated hip arthroplasty because of its unique advantages. We presented a case of a 59‐year‐old man with an intertrochanteric fracture in the left femur, who had received a prior left hip fusion. After admission to our hospital, a left total hip arthroplasty was performed on the patient using a combination of MR technology and 3D printing technology. Before surgery, 3D reconstruction of a certain bony landmark exposed in the surgical area was first performed. Then a veneer part was designed according to the bony landmark and connected to a reference registration landmark outside the body through a connecting rod. After that, the series of parts were made into a holistic reference registration instrument using 3D printing technology, and the patient's data for bone and surrounding tissue, along with digital 3D information of the reference registration instrument, were imported into the head‐mounted display (HMD). During the operation, the disinfected reference registration instrument was installed on the selected bony landmark, and then the automatic real‐time registration was realized by HMD through recognizing the registration landmark on the reference registration instrument, whereby the patient's virtual bone and other anatomical structures were quickly and accurately superimposed on the real body of the patient. To the best of our knowledge, this is the first report to use MR combined with 3D printing technology in total hip arthroplasty.     

中日友好医院分型股骨头坏死腓骨植入治疗的三维有限元分析

文题释义：股骨头坏死中日友好医院分型的有限元分析：根据李子荣等提出的中日友好医院分型，建立股骨头坏死三维模型，分为 M型(内侧型)、C型(中央型)和 L型(外侧型)，其中 L型包括L1型(次外侧型)、L2型(极外侧型)和 L3型(全头型)。通过对建立的模型进行有限元分析，为该分型的保髋治疗提供了一定力学依据，显示外侧柱的存留是精准预防塌陷的重要因素，为进一步实现个体化治疗提供力学基础。 腓骨支撑坏死股骨头保髋手术：是对于早中期股骨头坏死需要保留股骨头患者进行的一种手术方式。首先需对股骨头进行髓芯减压，清除一定坏死骨，空腔填塞松质骨(髂骨为主)，打压结实后植入腓骨(异体或自体)支撑，给坏死区的提供力学支撑及生物学修复，预防股骨头进一步坏死及塌陷。 背景：研究报道股骨头坏死的保髋疗效与外侧柱存留密切相关，中日友好医院分型是根据三柱结构确立的，对股骨头塌陷的预测准确性高。 目的：建立股骨头坏死中日友好医院分型各分型仿真的三维有限元模型，通过有限元分析各分型腓骨植入的力学变化，探讨外侧柱存留对保髋疗效的意义，为该分型的塌陷精准预测提供基础。 方法：建立正常股骨头、中日友好医院分型(M型、C型、L1型、L2型、L3型)股骨头坏死及其腓骨植入3组11种三维有限元模型，运用ANSYS软件进行有限元分析计算，观察各组模型的最大应力值、最大位移值及股骨头内部载荷传递模式。 结果与结论：①坏死组位移最大，应变最大，且因坏死分型不同而位移不同，位移变化如下：M型相似文献   

National level data analysis of facial lacerations in Korea using the National Health Insurance Service (NHIS) database

No national epidemiological investigations have been conducted recently regarding facial lacerations. The study was performed using the data of 3,634,229 people during the 5-year period from 2014 to 2018 archived by the National Health Information Database (NHID) of the Health Insurance Review and Assessment Service. Preschool and children under 10 years old accounted for about one-third of patients. Facial lacerations were concentrated in the “T-shaped” area, which comprised forehead, nose, lips, and the perioral area. The male to female ratio for all study subjects was 2.16:1. Age and gender are significantly related with each other (P < .001). Mean hospital stays decreased, and numbers of outpatient department visits per patient were highest for hospitals and lowest for health agencies. Over the study period, hospital costs per patient in tertiary and general hospitals increased gradually. Preschool and school-aged children are vulnerable to trauma. Male patients outnumbered female patients by a factor of more than 2. The “T-shaped’” area around forehead is vulnerable to injury. Total cost of medical care benefits per patient in tertiary hospitals was about 7 times on average than in health agencies. Regarding functional, behavioral, and aesthetic outcomes, more attention should be paid to epidemiologic data and hospital costs for facial lacerations.     

椎弓根螺钉三椎体内固定对脊椎爆裂性骨折患者椎体与神经功能状态的影响观察

目的:探讨椎弓根螺钉三椎体内固定对脊椎爆裂性骨折患者椎体与神经功能状态的影响.方法:选取2013年7月至2015年1月于本院进行治疗的58例脊椎爆裂性骨折患者为研究对象,对其进行回顾性研究,将其根据手术方式不同分为对照组(短节段椎弓根内固定组)29例和观察组(椎弓根螺钉三椎体内固定组)29例,然后将两组患者手术前与手术后不同时间的伤椎恢复指标与血清神经功能相关指标进行比较.结果:观察组手术后不同时间的伤椎恢复指标中的后凸Cobb角、椎体平移率、椎体高度丢失率与血清神经功能相关指标中的BDNF、NSE、NGF及S100B均显著好于同期的对照组指标,并且明显好于其治疗前,均有显著性差异(P＜0.05).结论:椎弓根螺钉三椎体内固定可更为有效地恢复脊椎爆裂性骨折患者椎体参数,并且更有助于神经功能状态的恢复.     

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.