主动脉出口血压对左心室血流特性的影响

采用Mimics对心脏进行三维重构，用3-matic进行模型优化以及模型误差分析，对左心室室壁运动做速度分布假设，基于UDF宏文件对左心室室壁运动编写程序，将血液视为非牛顿流体，采用动网格技术研究不同血压对左心室血液流动的影响。模拟发现当左心室收缩时，压力梯度明显，内部压力减小。当左心室舒张时，内部压力逐渐增高。二尖瓣口处的速率先增大后减小。血压升高，左心室内剪切应力持续增大，极易破坏红细胞结构，产生溶血现象，导致心脏功能紊乱。     

超高分辨率眼前节光学相干断层成像技术在结膜增生性疾病诊断中的应用

目的 探讨超高分辨率眼前节OCT在结膜增生性疾病诊断中的应用效果。方法 选取2017年1月～2018年12月浙江大学附属第一医院及北仑分院眼科门诊收治的150例(150眼)结膜增生性疾病患者作为研究组。另选同期在我院进行体检的结膜正常者80例(80眼)作为对照组。统计分析超高分辨率眼前节OCT在睑裂斑,翼状胬肉及鳞状上皮瘤等结膜增生性疾病中临床诊断与病理诊断的符合率。观察分析睑裂斑,翼状胬肉及鳞状上皮瘤等结膜增生性疾病的特征性表现。结果 150例(150眼)结膜增生患者临床诊断为睑裂斑患者30例(30眼),翼状胬肉患者100例(100眼),以及眼表鳞状细胞瘤的患者20例(20眼)。与病理学诊断相比,UHR-OCT诊断睑裂斑诊断符合率为100.00%(30/30),翼状胬肉诊断符合率100.00%(98/98),眼表鳞状细胞瘤诊断符合率为90.91%(20/22),经统计学处理,差异无统计学意义(P0.05)。睑裂斑患者图像特征:生长在角巩膜缘处停止,且角膜上未见隆起,与前弹力层间未见高反射信号,与巩膜间未见明显分界。翼状胬肉患者图像特征:结膜上皮厚度轻度增厚,上皮层表现为中等程度的高反射,角膜上皮与前弹力层间表现为较高程度的高反射信号。OSSN患者图像特征:结膜上皮增厚,呈高反射,结膜正常上皮与异常上皮转变突然,无过渡区域,深部组织间上皮内可见瘤变。结论 超高分辨率眼前节OCT在结膜增生性疾病的诊断中具有较高的准确性,且影像特征明显,具有广阔的临床应用前景。     

国内住培中医患沟通研究现状的分析与思考

目的通过文献计量法分析国内住院医师规范化培训(简称住培)中关于医患沟通的研究现状,为提高住培过程中医患沟通能力提出建设性对策。方法应用文献检索策略,在中文期刊全文数据库搜索并导出自2000年1月1日—2020年2月29日以来有关住培中医患沟通的全部文献,以文献标题、发表时间和研究内容等构建Excel数据库,并进行统计分析。结果查到住培相关医患沟通的相关文献共70篇,均于2009年以后发表,文献研究数量逐年增加;文献作者所在地域分布不均匀;作者单位主要以大学附属医院为主;文献多出自于教育类、临床类和管理类期刊;文献的研究方法主要以理论论述为主,缺乏干预性研究;文献的被引次数较低,文献质量相对较差;文献的研究内容主要对医患沟通的重要性、内涵建设、现状与挑战和提高医患沟通措施方面进行了阐述。结论医患沟通培训在住培中并未受到充分重视,需在住培期间加强医患沟通教育,培养医患沟通能力,为创建新型医患关系奠定坚实的基础。     

AI-assisted superresolution cosmological simulations

Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in artificial intelligence (AI; specifically deep learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data and then make accurate superresolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore, our results can be viewed as simulation realizations themselves, rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to 16 h−1Mpc and the HR halo mass function to within 10% down to 1011 M⊙. We successfully deploy the model in a box 1,000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy-formation physics in large cosmological volumes.

As telescopes and satellites become more powerful, observational data on galaxies, quasars, and the matter in intergalactic space becomes more detailed and covers a greater range of epochs and environments in the Universe. Our cosmological simulations (see, e.g., ref. 1) must also become more detailed and more wide-ranging in order to make predictions and test the effects of different physical processes and different dark-matter candidates. Even with supercomputers, we are forced to decide whether to maximize either resolution or volume, or else compromise on both. These limitations can be overcome through the development of methods that leverage techniques from the artificial intelligence (AI) revolution (see, e.g., ref. 2) and make superresolution (SR) simulations possible. In the present work, we begin to explore this possibility, combining knowledge and existing superscalable codes for petascale-plus cosmological simulations (3) with machine learning (ML) techniques to effectively create representative volumes of the Universe that incorporate information from higher-resolution models of galaxy formation. Our first attempts, presented here, involve simulations with dark matter and gravity only, and extensions to full hydrodynamics will follow. This hybrid approach, which will imply offloading simulations to neural networks (NNs) and other ML algorithms, has the promise to enable the prediction of quasar, supermassive black hole, and galaxy properties in a way that is statistically identical to full hydrodynamic models, but with a significant speed-up.Adding details to images below the resolution scale (SR image enhancement) has become possible with the latest advances in deep learning (DL; ML with NN; ref. 4), including generative adversarial networks (GANs; ref. 5). The technique has applications in many fields, from microscopy to law enforcement (6). It has been used for observational astronomical images by (7), to recover galaxy features from below the resolution scale in degraded Hubble Space Telescope images. Besides SR image enhancement, DL has started to find applications in cosmological simulations. For example, refs. 8 and 9 showed how NNs can predict the nonlinear formation of structures given simple linear theory predictions. NN models have also been trained to predict galaxies (10, 11) and 21-cm emission from neutral hydrogen (12) from simulations that only contain dark matter. GANs have been used in ref. 13 to generate image slices of cosmological models and to generate dark-matter halos from density fields (14). ML techniques other than DL find many applications, too. For example, Kamdar et al. (15) have applied extremely randomized trees to dark-matter simulations to predict hydrodynamic galaxy properties.Generating mocks for future sky surveys requires large volumes and high accuracy, a task that quickly becomes computationally prohibitive. To alleviate the cost, recently, Dai and Seljak (16) developed a Lagrangian-based parametric ML model to predict various hydrodynamical outputs from the dark-matter density field. In other work, Dai et al. (17, 18) sharpened the particle distribution using a potential gradient descent method starting from low-resolution (LR) simulations. Note, however, that these approaches did not aim to enhance the spatial or mass resolution of a simulation.On the DL side, recently, Ramanah et al. (19) explored using the SR technique to map density fields of LR cosmological simulations to that of the high-resolution (HR) ones. While the goal is similar, our work has the following three key differences. First, instead of focusing on the dark-matter density field, we aim to enhance the number of particles and predict their displacements, from which the density fields can be inferred. This approach allows us to preserve the particle nature of the N-body simulations and therefore to interpret the SR outputs as simulations themselves. Second, we test our technique at a higher SR ratio. Compared to ref. 19, which increased the number of Eulerian voxels by 8 times, we increase the number of particles and thus the mass resolution by a factor of 512. Finally, to facilitate future applications of SR on hydrodynamic simulations in representative volumes, we test our method at much smaller scales and in large simulations whose volume is much bigger than that of the training data.