医学论文CRF表

北京电大护理专业本科文献综述书写格式及要求（各位同学：请你仔细阅读以下内容进行综述文章的修改，整个文章的字数和参考文献要到达要求。）第一页：1．封面附页:北京电大护理学专业（小初号加黑黑体、居中）本科生毕业综述论文（初号加黑黑体、居中）题目：XXXXXXXXXXXXXXXXXXX（4号加黑宋体、居中）学 生：小3加黑宋体、居中左右对齐指导老师：_小3加黑宋体、居中左右对齐XXXX年XX月XX日（4号居中）第二页：2．题目举例： 题目可以是“××××××的研究进展”，或“××××××的综述”。3．摘要（不超过300字）：采用结构式摘要的陈述方式，包括：目的、方法、结果、结论。举例：目的：“探讨××××××的研究进展”。方法：通过文献检索的方法，总结和归纳××××××的研究进展。结果：这部分内容应该提炼出你在综述文章中的一些如：护理的观点、方法等内容。结论：通过综述对这个研究领域得到的结论。4．关键词：3~5个关键词 举例： 2型糖尿病 自我管理第三页以后：5．正文：包括前言、中心部分和小结。1）前言：主要阐明综述的立题依据和综述的目的，包括有关概念的界定、目前存在的问题或对主要问题争论的焦点、本文综述的范围及其必要性等。前言应简明、扼要，重点突出，起到概况和点明主题的作用，使读者对综述内容有一个初步了解，字数一般在200～300字左右。举例：癌因性疲乏的护理研究进展。前言: “癌因性疲乏（cancer-related fatigue,CRF）是由于癌症及其相关治疗引起患者长期紧张和痛苦而产生的一系列主观感觉，如虚弱、活动无耐力、注意力不集中、动力或兴趣减少等。本文对CRF的评估和护理干预研究进展进行综述，为采取针对性措施以减轻患者的癌因性疲乏提供依据。”2）中心部分： 是文献综述的重点和核心内容，一般按照写作提纲分成不同层次的小标题进行论述。在论述每个部分时，以每个小标题为主线，将相关文献的结果和观点归纳和综合在一起。举例：1. 书写中心部分时应注意下列问题：（1） 层次清晰：各层次小标题之间的关系应有逻辑性。例如，同一层次的小标题之间应为从属或包含关系。例：1 CRF概述 定义 流行病学特征（综述的文章开始最后有这部分内容如高血压的定义和流行病学特征等等） 2 CRF的评估 常用CRF评估量表 CRF的定性评估 3 CRF的护理干预 健康宣教 运动 认知行为干预 针灸治疗 饮食干预（这部分内容就要根据你写的内容列出小标题，以该标题作为主线进行讨论） （2）论述有理有据：在针对每个小标题进行论述时，应标引充分的文献作为论述依据，切忌主观臆断。举例：“ 饮食干预 国内李亚玲等研究发现，乳腺癌患者每天早晚进食补虚正气粥（黄芪20克、党参10克、粳米100克），服时酌加白糖，结合心理支持和有氧运动能有效缓解CRF症状。但是，该研究是几个干预项目的综合效果，饮食干预的效果无法单项评估。国外一项随机对照试验显示，通过2周的干预，与安慰剂组比较，鱼油组并不能改变疲乏症状，甚至大部分患者最后因嗳气和鱼油的余味而无法每日吞下十颗鱼油胶囊。早期有学者的随机对照试验发现，在家里实行饮食干预也不能改善患者的疲乏。因此，饮食干预能否减轻CRF还需进一步研究。” (3)对文献进行有机归纳：文献综述不是简单的文献罗列，应围绕某个小标题，恰当利用一些连接词，将各文献结果进行归纳和综合。如可按年代顺序，或观点的异同进行归纳，相同观点的文献可在一起表述。 （4）文献引用准确、客观：引用他人资料要正确，不可歪曲原作精神。（注意：参考文献的内容不要大段引用，此外标准参考文献的数字在右上角。如：国内李亚玲等研究发现[1]，乳腺癌患者每天早晚进食补虚正气粥）3）小结：是对综述的中心内容进行扼要总结。作者应对与该主题有关的各种观点进行综合评价，基于对文献内容的归纳和综合，提出自己的观点，指出存在的问题及今后发展的方向。举例：综上所述，CRF在癌症患者中普遍存在，其影响因素较多，作用机制复杂，目前还缺乏有效的治疗手段，护理对策亦未完善。医护人员应充分重视这个问题，关注CRF对患者生命质量的影响，并进行干预措施的研6．致谢：用简短诚恳的语言，对课题研究过程中给予自己直接或者间接指导和帮助的导师和其他人员、单位表示谢意，对课题给予资助者表示感谢。7．参考文献（不少于20篇，原则上采用近5年的文献）备注：（1）全文用小4宋体，倍行距（2）正文从第三页开始，每个标题另起一行（3）正文部分字数不少于8000字。（4）论文用A4纸打印，左侧装订（5）参考文献格式，参考文献按GB7714 87《文后参考文献著录规则》采用顺序编码制著录，依照其在文中出现的先后顺序用阿拉伯数字加方括号标出。参考文献中的作者，1～3名全部列出，3名以上只列前3名，后加“，等”或其他与之相应的文字。外文期刊名称用缩写，以《Index Medicus》中的格式为准；中文期刊用全名。每条参考文献均须著录起止页。参考文献必须由作者与其原文核对无误。将参考文献按引用先后顺序(用阿拉伯数字标出)排列于文末。 举例：【杂志】作者.文题.刊名，年份，卷(期)：起页-迄页. 例如： 1谭莉莉，黄津芳，王虹，等.康复训练对先天性心脏病患儿术后恢复的影响.中华护理杂志，1996，31(6)：314-315. 2 King LA ,Downey GO ,Potish RA ,et al .Treatment of advanced epithelial ovarian carcinoma in Pregnancy with cisplatin based chemotherapy .Gynecol Oncol,1991,41:78-80. 3 Levine SR ,Welch KM.抗磷脂抗体.陈芷若译.国外医学内科学分册，1990，17：267-269. 【书籍】主编者.书名.版次.卷次.出版地：出版者，年份.起页-迄页. 或作者.文题.见：主编者.书名.卷次.版次.出版地：出版者，年份.起页-迄页.例如： 1 陈新谦，金有豫主编.新编药物学.第13版.北京：人民卫生出版社，. 2 汪敏刚.支气管哮喘.见：戴自英主编.实用内科学.第8版.北京：人民卫生出版社， 840. 还有不明白的留言吧

论文: Generative adversarial network in medical imaging: A review 这篇文章发表于顶刊Medical Imaging Analysis 2019上，文章细数了GAN应用于医学图像的七大领域——重建（图像去噪）、合成、分割、分类、检测、配准和其他工作，并介绍了包括医学图像数据集、度量指标等内容，并对未来工作做出展望。由于笔者研究方向之故，本博客暂时只关注重建、合成部分的应用。关于该论文中所有列出的文章，均可在 GitHub链接 中找到。 GAN在医学成像中通常有两种使用方式。第一个重点是生成方面，可以帮助探索和发现训练数据的基础结构以及学习生成新图像。此属性使GAN在应对数据短缺和患者隐私方面非常有前途。第二个重点是判别方面，其中辨别器D可以被视为正常图像的先验知识，因此在呈现异常图像时可以将其用作正则器或检测器。示例(a)，(b)，(c)，(d)，(e)，(f)侧重于生成方面，而示例 (g) 利用了区分性方面。下面我们看一下应用到分割领域的文章。 (a)左侧显示被噪声污染的低剂量CT，右侧显示降噪的CT，该CT很好地保留了肝脏中的低对比度区域[1]。 (b)左侧显示MR图像，右侧显示合成的相应CT。在生成的CT图像中很好地描绘了骨骼结构[2]。 (c)生成的视网膜眼底图像具有如左血管图所示的确切血管结构[3]。(d)随机噪声（恶性和良性的混合物）随机产生的皮肤病变[4]。 (e)成人胸部X光片的器官（肺和心脏）分割实例。肺和心脏的形状受对抗性损失的调节[5]。 (f)第三列显示了在SWI序列上经过域调整的脑病变分割结果，无需经过相应的手动注释训练[6]。 (g) 视网膜光学相干断层扫描图像的异常检测[7]。 通常，研究人员使用像像素或逐像素损失（例如交叉熵）进行分割。尽管使用了U-net来组合低级和高级功能，但不能保证最终分割图的空间一致性。传统上，通常采用条件随机场（CRF）和图割方法通过结合空间相关性来进行细分。它们的局限性在于，它们仅考虑可能在低对比度区域中导致严重边界泄漏的 pair-wise potentials (二元势函数 -- CRF术语)。另一方面，鉴别器引入的对抗性损失可以考虑到高阶势能。在这种情况下，鉴别器可被视为形状调节器。当感兴趣的对象具有紧凑的形状时，例如物体，这种正则化效果更加显着。用于肺和心脏mask，但对诸如血管和导管等可变形物体的用处较小。这种调节效果还可以应用于分割器(生成器)的内部特征，以实现域（不同的扫描仪，成像协议，模态）的不变性[8、9]。对抗性损失也可以看作是f分割网络（生成器）的输出和 Ground Truth 之间的自适应学习相似性度量。因此，判别网络不是在像素域中测量相似度，而是将输入投影到低维流形并在那里测量相似度。这个想法类似于感知损失。不同之处在于，感知损失是根据自然图像上的预训练分类网络计算而来的，而对抗损失则是根据在生成器演变过程中经过自适应训练的网络计算的。 [10] 在鉴别器中使用了多尺度L1损失，其中比较了来自不同深度的特征。事实证明，这可以有效地对分割图执行多尺度的空间约束，并且系统在BRATS 13和15挑战中达到了最先进的性能。 [11] 建议在分割管道中同时使用带注释的图像和未带注释的图像。带注释的图像的使用方式与 [10] 中的相同。 [10] 和 [12] ，同时应用了基于元素的损失和对抗性损失。另一方面，未注释的图像仅用于计算分割图以混淆鉴别器。 [13] 将pix2pix与ACGAN结合使用以分割不同细胞类型的荧光显微镜图像。他们发现，辅助分类器分支的引入为区分器和细分器提供了调节。 这些前述的分割训练中采用对抗训练来确保最终分割图上更高阶结构的一致性，与之不同的是， [14] -- code 中的对抗训练方案,将网络不变性强加给训练样本的小扰动，以减少小数据集的过度拟合。表中总结了与医学图像分割有关的论文。 参考链接： [1] X. Yi, P. Babyn. Sharpness-aware low-dose ct denoising using conditional generative adversarial network. J. Digit. Imaging (2018), pp. 1-15 [2] . Wolterink, . Dinkla, . Savenije, . Seevinck, . van den Berg, I. Išgum. Deep MR to CT synthesis using unpaired data International Workshop on Simulation and Synthesis in Medical Imaging, Springer (2017), pp. 14-23 [3] P. Costa, A. Galdran, . Meyer, M. Niemeijer, M. Abràmoff, . Mendonça, A. Campilho. End-to-end adversarial retinal image synthesis IEEE Trans. Med. Imaging(2017) [4] Yi, X., Walia, E., Babyn, P., 2018. Unsupervised and semi-supervised learning with categorical generative adversarial networks assisted by Wasserstein distance for dermoscopy image classification. arXiv: . [5] Dai, W., Doyle, J., Liang, X., Zhang, H., Dong, N., Li, Y., Xing, ., 2017b. Scan: structure correcting adversarial network for chest x-rays organ segmentation. arXiv: . [6] K. Kamnitsas, C. Baumgartner, C. Ledig, V. Newcombe, J. Simpson, A. Kane, D. Menon, A. Nori, A. Criminisi, D. Rueckert, et al. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks International Conference on Information Processing in Medical Imaging, Springer (2017), pp. 597-609 [7] T. Schlegl, P. Seeböck, . Waldstein, U. Schmidt-Erfurth, G. Langs Unsupervised anomaly detection with generative adversarial networks to guide marker discovery International Conference on Information Processing in Medical Imaging, Springer (2017), pp. 146-157 [8] K. Kamnitsas, C. Baumgartner, C. Ledig, V. Newcombe, J. Simpson, A. Kane, D. Menon, A. Nori, A. Criminisi, D. Rueckert, et al. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks International Conference on Information Processing in Medical Imaging, Springer (2017), pp. 597-609 [9] Dou, Q., Ouyang, C., Chen, C., Chen, H., Heng, ., 2018. Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss. arXiv: . [10] Y. Xue, T. Xu, H. Zhang, . Long, X. Huang Segan: adversarial network with multi-scale l 1 loss for medical image segmentation Neuroinformatics, 16 (3–4) (2018), pp. 383-392 [11] Y. Zhang, L. Yang, J. Chen, M. Fredericksen, . Hughes, . Chen. Deep adversarial networks for biomedical image segmentation utilizing unannotated images International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2017), pp. 408-416 [12] Son, J., Park, ., Jung, ., 2017. Retinal vessel segmentation in fundoscopic images with generative adversarial networks. arXiv: . [13] Y. Li, L. Shen. CC-GAN: a robust transfer-learning framework for hep-2 specimen image segmentation IEEE Access, 6 (2018), pp. 14048-14058 [14] W. Zhu, X. Xiang, . Tran, . Hager, X. Xie. Adversarial deep structured nets for mass segmentation from mammograms 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE (2018) [15] D. Yang, D. Xu, . Zhou, B. Georgescu, M. Chen, S. Grbic, D. Metaxas, D. Comaniciu. Automatic liver segmentation using an adversarial image-to-image network International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2017), pp. 507-515 [16] Dou, Q., Ouyang, C., Chen, C., Chen, H., Heng, ., 2018. Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss. arXiv: . [17] Rezaei, M., Yang, H., Meinel, C., 2018a. Conditional generative refinement adversarial networks for unbalanced medical image semantic segmentation. arXiv: . [18] A. Sekuboyina, M. Rempfler, J. Kukačka, G. Tetteh, A. Valentinitsch, . Kirschke, . Menze. Btrfly net: Vertebrae labelling with energy-based adversarial learning of local spine prior International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham (2018) [19] M. Rezaei, K. Harmuth, W. Gierke, T. Kellermeier, M. Fischer, H. Yang, C. Meinel. A conditional adversarial network for semantic segmentation of brain tumor International MICCAI Brainlesion Workshop, Springer (2017), pp. 241-252 [20] P. Moeskops, M. Veta, . Lafarge, . Eppenhof, . Pluim. Adversarial training and dilated convolutions for brain MRI segmentation Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer (2017), pp. 56-64 [21] Kohl, S., Bonekamp, D., Schlemmer, ., Yaqubi, K., Hohenfellner, M., Hadaschik, B., Radtke, ., Maier-Hein, K., 2017. Adversarial networks for the detection of aggressive prostate cancer. arXiv: . [22]Y. Huo, Z. Xu, S. Bao, C. Bermudez, . Plassard, J. Liu, Y. Yao, A. Assad, . Abramson, . Landman. Splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks Medical Imaging 2018: Image Processing, 10574, International Society for Optics and Photonics (2018), p. 1057409 [23]K. Kamnitsas, C. Baumgartner, C. Ledig, V. Newcombe, J. Simpson, A. Kane, D. Menon, A. Nori, A. Criminisi, D. Rueckert, et al. Unsupervised domain adaptation in brain lesion segmentation with adversarial networks International Conference on Information Processing in Medical Imaging, Springer (2017), pp. 597-609 [24]Z. Han, B. Wei, A. Mercado, S. Leung, S. Li. Spine-GAN: semantic segmentation of multiple spinal structures Med. Image Anal., 50 (2018), pp. 23-35 [25]M. Zhao, L. Wang, J. Chen, D. Nie, Y. Cong, S. Ahmad, A. Ho, P. Yuan, . Fung, . Deng, et al. Craniomaxillofacial bony structures segmentation from MRI with deep-supervision adversarial learning International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer (2018), pp. 720-727 [26] Son, J., Park, ., Jung, ., 2017. Retinal vessel segmentation in fundoscopic images with generative adversarial networks. arXiv: . [27]Y. Li, L. Shen. CC-GAN: a robust transfer-learning framework for hep-2 specimen image segmentation IEEE Access, 6 (2018), pp. 14048-14058 [28] S. Izadi, Z. Mirikharaji, J. Kawahara, G. Hamarneh. Generative adversarial networks to segment skin lesions Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on, IEEE (2018), pp. 881-884 Close [29]W. Zhu, X. Xiang, . Tran, . Hager, X. Xie. Adversarial deep structured nets for mass segmentation from mammograms 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), IEEE (2018)

久坐对健康危害程度大过心脏病，这个确实是，因为人一般坐着两小时后应该起来多活动活动，不然这个不好的习惯还会成为引起我们身体患各种不明的隐患疾病。

Through the study of animal experiments to observe the United Yishen soup benazepril 5 / 6 nephrectomized rats renal function improved efficacy and reduced TGF-β1 expression in renal tissue and other advantages, and to explore the soup Yishen possible to improve the renal function of mechanism of ：1. Select SPF class healthy adult male SD rats with 50 only as the research object, adaptive feeding one weeks later, 10 randomly selected as the sham-operated group (A group), and the remaining 40 with 5 / 6 nephrectomy CRF-made law the success of model selection criteria in line with the study group 32 rats were divided into B: model group, 8; C groups: benazepril group 8; D group: the Chinese Medicines Board 8; E group: beneficial United kidney soup benazepril group referred to as traditional Chinese and western medicine group 8, together with the A Group of 5 . Successful modeling start after delivery, the groups were given corresponding drugs . The end of the experiment 24 hours after the detection of urinary protein and blood BUN, Scr, RBC, Hb, and renal histology observation and renal tissue TGF-β1 expression general situation: during the experiment, sham-operated rats demonstrated alertness, quick reaction, dense fur, clean and shiny, growth, consumption and the activities had no significant abnormalities, weight gain; model group was significant malnutrition, make them apathetic, slow activity , loss of appetite, fluffy fur, haggard Matte, died in the course of treatment at 2, probably because of renal failure due consideration; benazepril rats than sham-operated group spirit apathetic, slow activity, fur, fluffy; Chinese Medicines large mouse performance and a similar benazepril group; WM rats with sham-operated rats without much difference in general performance, but dry dark . Of blood BUN, Scr impact: benazepril group, traditional Chinese medicine group, in the WM group significantly decreased BUN, Scr level, compared with the model group has significant difference (P <), but still higher than sham-operated group; traditional Chinese and western medicine group and the Chinese medicine group, benazepril group has significant difference (P <); Chinese medicine group and the benazepril group was no significant difference ( P> ).3. Hematology impact: Chinese medicine group and the TCM-WM group was significantly increased blood RBC, Hb, compared with the model group has statistically significant difference (P <), but the difference between the two groups was not significant (P> ); benazepril group compared with the model group was no significant difference (P> ).4. Pathologic changes, acceptance of renal rat subtotal excision were visible matrix hyperplasia, glomerular sclerosis, but the model group compared to the treatment group significantly lesser degree of glomerular sclerosis, one of traditional Chinese and western medicine to renal small ball for the lightest sclerosis; Immunohistochemistry results showed that the treatment group in renal tissue expression of TGF-β1 were significantly lower than model group (P <), and traditional Chinese and western medicine group can reduce the TGF-B1 in renal tissues, with the Chinese medicine group and the benazepril group has statistically significant difference (P <). Conclusion: Yishen soup through Yiqi Jianpi, huoxuehuayu, dampness Xiexin Turbidimetry, CRF can reduce blood BUN, Scr, improve anemia and reduce proteinuria, can be reduced effectively with 5 / 6 nephrectomy-induced CRF rat kidney tissue expression of TGF-β1, thereby reducing the accumulation of ECM, slowing the development of renal fibrosis, and delay the progress of Yishen soup has a good anti-renal fibrosis, but also after the United benazepril better efficacy.

久坐是最常见的一个现象，但也是最不健康的一种生活方式。因为它可能导致某些慢性病的风险。但它的危害大不过心脏病。