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   

Three-dimensional measurement of radiographic bone–implant contact lengths of zygomatic implants in zygomatic bone: a retrospective study of 66 implants in 28 patients

Zygomatic implant treatment is widely applied for severe maxillary atrophy to help rehabilitate the maxillary dentition. This retrospective study was performed to evaluate the actual radiographic bone–implant contact (rBIC) lengths of zygomatic implants. The records of 28 patients who underwent zygomatic implant surgery and subsequent follow-up examinations between August 2013 and September 2018 in the Department of Oral and Maxillofacial Surgery, Taipei Tzu Chi Hospital were reviewed. The surgeries were performed by a single surgeon using the same treatment protocol. All patients had a computed tomography scan at 1 year after the surgery. Using three-dimensional imaging software, an investigator measured the rBIC lengths of 66 implants and documented their clinical status. The implant survival rate was 100%. The mean rBIC length was significantly longer in male patients than in female patients (20.80 ± 5.88 mm versus 17.79 ± 6.34 mm; P = 0.028). The mean rBIC length of double zygomatic implants was significantly longer when compared to that of single implants (21.11 ± 6.23 mm versus 17.75 ± 5.85 mm; P = 0.027). This article is novel in reporting the exact rBIC lengths of zygomatic implants in a clinical setting. The results showed that zygomatic implants are a viable treatment modality for full-mouth rehabilitation.     

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.     

颈前入路相关结构三维可视化研究

目的：建立颈前入路相关结构的三维可视化模型。方法：选取首例中国数字化可视人体数据集中第3颈椎上缘至第7颈椎下缘的连续薄层断面图像。运用3D—DOCTOR软件，在计算机上分割重建该手术入路相关的解剖结构并立体显示。结果：成功重建并立体显示第3～7颈椎、椎间盘、脊髓、颈神经、椎动脉、颈长肌、喉（气管）、咽（食管）、颈总动脉（颈内、外动脉）、颈内静脉、迷走神经和胸锁乳突肌等解剖结构的位置关系，建立了颈前入路相关结构的三维可视化模型。结论：在颈部三维可视化模型基础上，参照颈前入路手术步骤可逐层显示该手术相关的重要结构．为该手术的术前训练和模拟提供形态学依据。     

实时三维超声对室间隔缺损的直视效果及影响因素分析

目的探讨实时三维超声（RT3DE）对室间隔缺损（VSD）全貌的直视效果及其影响因素。 方法应用RT3DE对238例VSD患者进行检查，其中包括法洛四联症（TOF）36例、完全型心内膜垫缺损（TECD）2例和十字交叉心2例。获取并切割“全容积三维”图像，寻找最佳视角显示病变结构全貌。 结果RT3DE对中型和大型VSD（直径≥5mm）的直视效果优于小型VSD（〈5mm）。可显示缺损的形状、部位及毗邻结构。胸骨旁四腔位和心底短轴位RT3DE图像效果最佳，胸骨旁长轴位及心尖五腔位次之。 结论VSD的大小、部位及三维图像质量是决定RT3DE观察效果的主要因素。RT3DE可清晰直视中型及大型VSD的全貌。     

原发性肝癌的彩色多普勒及三维血流能量成像特征及其临床意义

目的:探讨三维血流能量成像(3D-CPA)和彩色多普勒血流成像(CDFI)的临床应用价值。方法:对47例原发性肝癌的病灶进行二维、CDFI及3D-CPA的检查,比较CDFI及3D-CPA显示肿瘤瘤内或瘤周血流多普勒信号丰富程度及血管分布类型,并检测肿瘤的血流参数。结果:3D-CPA显示肿瘤血流分布丰富程度及血管分布类型与CDFI均有显著性差异(均P<0.05),CDFI均可探及肿瘤的搏动性动脉血流,其中,收缩期峰值血流速度(PSV)为43~157cm/s,阻力指数(RI)为0.49~0.82,大于0.65者45例。结论:3D-CPA结合彩色多普勒血流参数的检测可作为临床评价原发性肝癌血供及血管分布的常规检查方法。     

膝关节运动过程的应力数值模拟

目的观察膝关节关节面在整个运动过程中的应力、应变分布范围、大小及变化规律。方法利用有限元数值模拟方法对行走过程中膝关节关节面应力、应变情况进行分析。结果与结论行走过程中关节面产生应力、应变随时间推移而增加，并且变化规律近似的服从抛物线变化。通过对不同体重的人在行走时膝关节面上产生的应力分析得出，体重对关节面上的应力的大小并不产生重大影响，体重带来的应力差异仅占总应力的10％。     

用头孢西丁三相试验检测大肠埃希菌及肺炎克雷伯菌的质粒AmpC酶

目的：检测质粒介导的持续高产AmpC酶在大肠埃希菌及肺炎克雷伯菌中的携带率，为临床治疗提供指导。方法：采用头孢西丁纸片药敏试验（K-B法）作AmpC酶的初筛试验，头孢西丁三相试验间接法作AmpC酶的确证试验。结果：在检测的86株菌中，有9株菌K-B法初筛结果为阳性。在这9株菌中经头孢西丁三相试验确证有5株产AmpC酶，阳性率为5.8%。其中大肠埃希菌3株，肺炎克雷伯菌2株。结论：头孢西丁K-B法筛选试验结合头孢西丁三相试验可准确检出质粒介导的AmpC酶，可用于临床常规检测。