新冠肺炎“动态清零”常态化下应急梯队护士体验的质性研究

目的 研究新型冠状病毒肺炎常态化疫情防控下应急梯队护士的真实体验。提出针对性改善护士的心理状况的方法和建议。方法 于2021年10月选取上海市静安区闸北中心医院应急梯队的护士11名为研究对象，对其进行半结构式面对面谈话，并采用Colaizzi七步分析法进行资料分析。结果 进入应急梯队的护士普遍存在着紧张焦虑，情绪低落，身心疲惫等心理问题。而执行完应急任务后的成就感，社会的认同，多方的理解支持，有利缓解心理压力。结论 通过对新型冠状病毒肺炎常态化疫情防控下应急梯队护士的真实体验的了解，护士普遍存在各种心理问题，需要给予针对性的干预措施，提高应急梯队成员的心理素质和应急能力。     

Simultaneous multi‐slice spin‐ and gradient‐echo dynamic susceptibility‐contrast perfusion‐weighted MRI of gliomas

Although combined spin‐ and gradient‐echo (SAGE) dynamic susceptibility‐contrast (DSC) MRI can provide perfusion quantification that is sensitive to both macrovessels and microvessels while correcting for T₁‐shortening effects, spatial coverage is often limited in order to maintain a high temporal resolution for DSC quantification. In this work, we combined a SAGE echo‐planar imaging (EPI) sequence with simultaneous multi‐slice (SMS) excitation and blipped controlled aliasing in parallel imaging (blipped CAIPI) at 3 T to achieve both high temporal resolution and whole brain coverage. Two protocols using this sequence with multi‐band (MB) acceleration factors of 2 and 3 were evaluated in 20 patients with treated gliomas to determine the optimal scan parameters for clinical use. ΔR₂*(t) and ΔR₂(t) curves were derived to calculate dynamic signal‐to‐noise ratio (dSNR), ΔR₂*‐ and ΔR₂‐based relative cerebral blood volume (rCBV), and mean vessel diameter (mVD) for each voxel. The resulting SAGE DSC images acquired using MB acceleration of 3 versus 2 appeared visually similar in terms of image distortion and contrast. The difference in the mean dSNR from normal‐appearing white matter (NAWM) and that in the mean dSNR between NAWM and normal‐appearing gray matter were not statistically significant between the two protocols. ΔR₂*‐ and ΔR₂‐rCBV maps and mVD maps provided unique contrast and spatial heterogeneity within tumors.     

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.     

从“筋骨、痹痿、虚实、动静、刚柔”谈膝骨关节炎功能障碍的特点

膝骨关节炎(KOA)属于进展性骨关节病,其功能障碍主要表现为膝关节疼痛、僵硬、屈伸行走不利或受限,以及关节失稳、运动控制下降和本体感觉低下等。我国KOA患者众多,且还在逐年增加,引起的功能障碍严重影响着患者健康与生活质量,因此对其开展康复的研究与实践至关重要。中医康复着眼于功能,注重辨证康复,是我国康复医学的固有特色及优势。前期的研究基于KOA功能障碍的中医证候表现,本研究经大量文献梳理及多年临床实践,从“筋骨、痹痿、虚实、动静、刚柔”5个角度为切入点,提出KOA功能障碍具有“筋骨同病、痹痿并见、虚实错杂、动静失衡、刚柔失常”5个基本特点。鉴于KOA功能障碍具有早、中、晚三期的阶段性特点,在该病发生发展的不同阶段,其功能障碍在“筋骨、痹痿、虚实、动静、刚柔”之间的表现又各有所侧重。本研究从“筋骨同病、痹痿并见、虚实错杂、动静失衡、刚柔失常”5个方面分别阐释了KOA功能障碍的特点:“筋骨同病”侧重于病位,是KOA所致功能障碍的基本病机特征,贯穿于其发病的始终;“痹痿并见、虚实错杂”偏重病性,是KOA功能障碍的基本特点;“动静失衡”偏重病因,是引起KOA病情反复及加重的重要因素;“刚柔失常”则是兼具病状与病因,是膝关节失去“骨正筋柔”的表现方式,也是KOA功能障碍的重要特征。本研究旨在进一步完善KOA的中医康复理论,为其康复治疗提供参考。