Effect of CRRT combined with low-flow ECMO on canines with ARDS and hypercapnia

Journal of Artificial Organs - To observe the effect of continuous renal replacement therapy (CRRT) combined with low-flow extracorporeal membrane oxygenation (ECMO) of V–V mode on...     

异基因造血干细胞移植患者生活质量变化轨迹及影响因素研究

目的 探讨异基因造血干细胞移植患者3年内的生活质量变化轨迹及影响因素。方法 采用便利抽样法,选取2016年7月—2017年10月于苏州市某三级甲等医院行异基因造血干细胞移植的患者作为调查对象,在患者移植前（入仓前1~2 d）、移植后1个月、3个月、半年、1年、1年半及3年时采用一般资料调查表及癌症治疗功能评价-骨髓移植测评量表进行调查。 结果 异基因造血干细胞移植患者的癌症治疗功能评价-骨髓移植测评量表总分在移植后1个月时降至最低,此后呈逐渐上升趋势,半年时基本恢复至移植前水平,3年时总分显著高于移植前,差异具有统计学意义（P<0.05）。生理健康、功能健全及干细胞移植3个维度得分与癌症治疗功能评价-骨髓移植测评量表总分变化轨迹一致,呈波动上升型;情绪稳定维度在移植前得分最低,随移植时间延长逐渐上升;社交/家庭健全维度移植前得分与移植后各时间点得分比较,差异无统计学意义（P>0.05）。多元线性回归分析结果显示,影响患者移植后3年时生活质量的主要因素为居住地（t=3.175,P=0.002）、家庭人均月收入（t=3.320,P=0.001）、移植相关并发症（t=-6.955,P<0.001）及患者是否回归工作/学习（t=2.706,P=0.008）。 结论 异基因造血干细胞移植患者总体生活质量呈波动上升趋势,癌症治疗功能评价-骨髓移植测评量表5个维度间的恢复时间及变化轨迹存在差异。至移植后3年时,居住地为农村、家庭人均月收入较低、患有移植相关并发症及尚未回归工作/学习的患者生活质量更差。护理人员可根据患者生活质量变化轨迹动态调整护理措施,为其提供更为精准的延续性护理。     

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.     

从肝论治肠腑病的理论探讨＊

中医肠腑病多为肠道传化失常而出现的一系列肠腑病证。随着社会发展，人们情感心理障碍问题日益凸显，使得情志因素成为现代肠腑病发生与复发的重要影响因素，情志失常则会引起肝气或郁结或亢逆，致肝木横克脾土水湿不运而发肠腑病。因此，本文结合现代肠腑病的发病特点，通过论述肝与大肠之间的生理病理联系，分析从肝论治肠腑病的理论与临床依据，从调情志、舒肝气的角度论治肠腑病，为扩展临床治疗肠腑病的处方用药与针灸选穴思路提供借鉴与参考。     

社区卫生院护士静脉治疗知识技能横断面调查分析

目的了解社区卫生院护士对静脉治疗相关知识的掌握现状及实际操作水平。方法采用整群抽样方法,选择13家社区卫生院255名护士进行静脉治疗相关知识调查,并采取现场查看及抽考的方式,了解卫生院所使用的冲封管液肝素浓度和护士冲封管操作掌握情况。结果社区卫生院护士静脉治疗相关知识标准分17. 39～95. 65(53. 21±15. 03)分,60分以下有183人,占71. 76%。仅4家(30. 77%)卫生院使用的冲封管液稀肝素浓度0～10 U/m L,符合《静疗规范》要求。共计39名社区卫生院进行冲封管操作演示,护士脉冲式冲管的正确率为28. 20%;正压带液封管执行正确率为23. 08%。结论社区卫生院护士对静脉治疗相关知识的掌握情况欠佳,冲封管操作欠规范。护理管理者需要针对性地制订培训方案,并注重效果反馈和质量监督,规范护士的静脉治疗技术操作,促进静脉治疗护理安全。     

Clinical Practice Guidance: Surveillance for phaeochromocytoma and paraganglioma in paediatric succinate dehydrogenase gene mutation carriers

The succinate dehydrogenase (SDH) enzyme complex functions as a key enzyme coupling the oxidation of succinate to fumarate in the citric acid cycle. Inactivation of this enzyme complex results in the cellular accumulation of the oncometabolite succinate, which is postulated to be a key driver in tumorigenesis. Succinate accumulation inhibits 2‐oxoglutarate‐dependent dioxygenases, including DNA and histone demethylase enzymes and hypoxic gene response regulators. Biallelic inactivation (typically resulting from one inherited and one somatic event) at one of the four genes encoding the SDH complex (SDHA/B/C/D) is the most common cause for SDH deficient (dSDH) tumours. Germline mutations in the SDHx genes predispose to a spectrum of tumours including phaeochromocytoma and paraganglioma (PPGL), wild type gastrointestinal stromal tumours (wtGIST) and, less commonly, renal cell carcinoma and pituitary tumours. Furthermore, mutations in the SDHx genes, particularly SDHB, predispose to a higher risk of malignant PPGL, which is associated with a 5‐year mortality of 50%. There is general agreement that biochemical and imaging surveillance should be offered to asymptomatic carriers of SDHx gene mutations in the expectation that this will reduce the morbidity and mortality associated with dSDH tumours. However, there is no consensus on when and how surveillance should be performed in children and young adults. Here, we address the question: “What age should clinical, biochemical and radiological surveillance for PPGL be initiated in paediatric SDHx mutation carriers?”.