全结构化电子护理记录单的开发与临床应用评价

目的开发全结构化电子护理记录单,评价其应用效果。方法设计全结构化电子护理记录单并在南院区投入使用,北院区仍采用自由文本输入的方式记录电子护理记录单。实施1年后从2个院区各抽取230份出院病历对比护理记录,通过查看录像资料对比护士的记录效率。结果南院区护理记录结构正确率、符合专科护理指南率、护理诊断/评价及护理措施记录量与合格率显著高于北院区(均P<0.01);2个院区相同科室(内分泌科、儿科除外)护理记录时间比较,差异有统计学意义(均P<0.01)。结论全结构化电子护理记录单能提高护理书写质量和数据质量,但用于部分专科会增加记录时间,仍需要进一步优化。     

相变型纳米粒在动脉血栓的溶栓效果与靶向深度相关性的实验研究

目的 探讨相变型（PT）纳米粒（NPs）靶向动脉血栓深度及其与体外溶栓效果的相关性。方法 通过双乳化法制备一种靶向纤维蛋白的PLGA-PFH-CREKA NPs，验证其理化特性。采集新西兰大白兔的动脉血制作动脉血栓，分别置于荧光标记的靶向PT、非靶向PT、靶向载双蒸水（NPT）的NPs中，并用1 W/cm2声功率密度的低强度聚焦超声（LIFU）辐照，记录前后的质量，计算溶栓率，同时将处理后的血凝块制成切片，通过共聚焦显微镜观察、测量穿透深度，线性回归分析穿透深度与溶栓率的相关性。建立SD大鼠腹主动脉血栓模型，采用靶向PT、非靶向PT NPs评价体内靶向能力。结果 制备的靶向纤维蛋白的PT NPs平均粒径（297.8±11.82）nm，表面电位（1.44±0.22）mV，结构呈均匀球形，分散性好；体外溶栓实验发现靶向PT组与非靶向PT组、NPT组的溶栓率差异有统计学意义（F=108.508，P<0.001）；靶向PT组对血栓的穿透性相较于非靶向PT组、NPT组差异有统计学意义（F=96.187，P<0.001），线性回归发现靶向PT组对血栓穿透深度与溶栓率呈正相关（R2=0.818，P<0.05）。在SD大鼠腹主动脉血栓模型观察到靶向相变组较非靶向相变组对血栓的靶向性更好。 结论 靶向纤维蛋白的相变型NPs，对血栓纤维蛋白有良好的靶向性，有较好的溶栓效果，对血栓有很好的穿透性，并且相变所致溶栓率与血栓穿透深度呈正相关。     

Peripheral blood inflammatory markers in predicting prognosis in patients with COVID‐19. Some differences with influenza A

BackgroundTo evaluate the ability of peripheral blood inflammatory markers in predicating the typing of COVID‐19, prognosis, and some differences between COVID‐19 and influenza A patients.MethodsClinical data on 285 cases laboratory‐confirmed as SARS‐CoV‐2 infection were obtained from a Wuhan local hospital''s electronic medical records according to previously designed standardized data collection forms. Additional 446 Influenza A outpatients’ hematologic data were enrolled for comparison.ResultsNLR, SII, RLR, PLR, HsCRP, and IL‐6 were significant higher and LMR was lower in severe COVID‐19 patients than in mild COVID‐19 patients (p < .001). PLR and LMR were lower in the individuals with influenza A than those with COVID‐19 (p < .01). COVID‐19 patients with higher levels of NLR, SII, RLR, PLR, HsCRP, and IL‐6 and lower LMR were significantly associated with the severe type. AUC of NLR (0.76) was larger while the specificity of IL‐6 (86%) and sensitivity of HsCRP (89%) were higher than other inflammatory markers in predicating the typing of COVID‐19. PT had obvious correlation with all the inflammatory markers except RPR. NLR showed positive correlations with AST, TP, BUN, CREA, PT, and D‐dimer. Patients with high IL‐6 levels have a relatively worse prognosis (HR = 2.30).ConclusionPeripheral blood inflammatory markers reflected the intensity of inflammation and associated with severity of COVID‐19.NLR was more useful to predict severity as well as IL‐6 to predict prognosis of COVID‐19. PLR and LMR were initially found to be higher in SARS‐CoV‐2 virus‐infected group than in influenza A.     

Does Tweeting Improve Citations? One-Year Results From the TSSMN Prospective Randomized Trial

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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.     

血脂异常患者中医体质与钙镁锌的相关性研究＊

目的 探讨血脂异常患者中医体质与钙、镁、锌之间的相关性。方法 2019年4月至2020年10月在上海熙康门诊部收集血脂异常患者200例，血脂正常人群100例为对照组。检测血脂指标（TC、TG、LDL-C、HDL-C）及血钙、镁、锌指标，并对血脂异常组进行中医体质评估。使用方差分析、多元线性回归和Logistic回归分析异常组体质类型与钙、镁、锌及血脂指标的相关性。结果 ①血脂异常组钙、镁、锌水平均低于对照组（P < 0.001）；②不同体质之间的钙均值有差异（P < 0.001）；③多元线性回归分析显示钙与LDL-C相关（P = 0.039），锌与TC相关（P = 0.03）；④Logistic回归分析显示钙与阳虚质及痰湿质相关，TG与阳虚质相关（P < 0.001），TC与痰湿质相关（P < 0.001）。结论 血脂异常患者血钙、镁、锌微量元素水平及血脂指标与中医体质分型存在一定相关。