黄热病的再流行与世界卫生组织的消除黄热病流行战略

近年来,蚊虫传播的黄热病在非洲和南美洲地区大面积“再流行”,仅南美洲的巴西在一年时间就发现数千例黄热病确诊病例,黄热病已经在非洲和南美洲地区造成巨大的公共卫生负担。此外,在黄热病非流行国家和地区发现大量输入性病例,使得黄热病有可能在非洲和南美洲以外的地区成为“新发传染病”而在世界更大范围流行。为此,世界卫生组织制定了“消除黄热病流行（EYE）”战略,预计利用10年时间在黄热病流行地区接种13.8亿剂量黄热疫苗,以降低黄热病对国际公共卫生健康的危害。     

调整Kappa角及光学切削直径对准分子激光原位角膜磨镶术后的影响

目的　评价调整光学切削直径及Kappa角后对准分子激光原位角膜磨镶术（laser in situ keratomileusis，LASIK）后效果的影响。方法　选取2017年1月至12月在我院行LASIK手术的高度近视患者313例（626眼），根据切削直径分成两组，试验组157例314眼，切削直径设定为6.0 mm，对照组156例312眼，切削直径设定为6.5 mm。试验组患者激光切削前修正Kappa角，对照组不做修正。患者术前进行裸眼视力、主视眼确定、验光、眼压、暗室下瞳孔直径、泪液分泌试验、裂隙灯、散瞳验光、眼底检查、pentacam测量角膜厚度、角膜地形图测量角膜前后表面及Kappa角等检查。术后1 d、1周、1个月随访，并检查裸眼视力、角膜厚度、波前像差及夜间视力、光晕、眩光等情况。比较两组患者角膜厚度变化、手术所用时间以及两组患者术后的高阶像差的差异。结果　试验组与对照组患者年龄分别为18～44（24.19±5.33）岁、18～42（25.08±4.91）岁，屈光度分别为（-7.47±1.04）D、（-7.61±1.12）D。两组年龄、屈光度比较差异均无统计学意义（均为P>0.05）。试验组与对照组患者术前Kappa角分别为，X轴:（210±40）μm、（200±30）μm，Y轴:（190±30）μm、（220±40）μm，差异无统计学意义（P=0.210）。两组手术前后的角膜厚度及术后角膜基质床的厚度差异均无统计学意义（均为P>0.05）。试验组与对照组的手术时间分别为（15.56±1.89）s和（20.83±3.03）s，差异有统计学意义（P=0.000）。试验组的总高阶像差和垂直慧差的变化均明显低于对照组（均为 P<0.01），但两组间的水平慧差差异无统计学意义（P>0.05），对照组的球差低于试验组（P<0.01）。结论　LASIK手术中科学合理地调整Kappa角可有助于提高患者术后的视觉质量。     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

足趾移植长手指全形再造术的临床应用

目的探讨足趾移植长手指全形再造手术的临床疗效。方法自2015年6月至2019年6月,对16例因外伤致手指缺损患者采用足趾移植长手指全形再造术,术后评估供区及受区的感觉功能、运动功能及外观形态,分析指甲畸形及增生性瘢痕的发生情况,并记录术后发生感染、血肿、皮片坏死及供区愈合不良等情况;通过调查问卷的方式分析患者的满意度。结果所有患者术后获随访1~12个月,其中2例受区发生感染,1例受区皮片边缘发生坏死,经换药后予以缓解;其余患者的供、受区均未出现长时间的痛疼感觉,受区感觉功能恢复达87.50%,受区运动功能恢复均较满意,手指外形基本满意。供区感觉受影响者2例,运动功能受限者1例,外形一般者2例。所有患者无指甲畸形及增生性瘢痕发生;满意者1例,基本满意者14例,不满意者1例。结论采用足趾移植长手指全形再造手术,基本可以满足患者及医师对于缺损手指进行完美修复的目标。     

镇江市护士分层培训及分层进阶现况分析

目的调查镇江市各医院护士分层培训及分层进阶的方法及现状。方法2018年4—5月间采用自行设计的调查问卷,对全市医院的护理人员进行随机抽样调查。结果调查显示镇江市96.45%的护士受医院分层培训及分层进阶这一模式的管理。其中N1占16.77%,N2占29.5%,N3占41.9%,N4占8.7%,其他占3.11%。有24.53%的护士对医院目前的分层培训方式感到满意并认为无需改进,40.99%的护士表示满意,但需要改进,29.19%的护士表示基本满意,需要改进,5.28%的护士表示不满意需要较大改进。95.82%的医院科室对不用层级的护士有不同的核心能力要求并根据不同核心能力要求进行培训。结论护士分层培训几分层进阶这一管理模式在镇江各医院都有体现,但各医院实行的方式有较大差异,配套的管理方式也有待完善。     

指压天枢穴加腹部按摩联合番泻叶在静脉肾盂造影患者中的应用效果

目的探讨指压天枢穴加腹部按摩联合番泻叶在静脉肾盂造影患者中的应用效果。方法选取我院2018年12月至2019年8月期间行静脉肾盂造影的120例患者,随机分为两组各60例。对照组给予番泻叶,观察组给予天枢穴指压加腹部按摩联合番泻叶,比较两组的肠道清洁力度和不良反应情况。结果治疗后,观察组的肠道清洁力度明显高于对照组(P <0.05);观察组的不良反应发生率为6.67%,明显低于对照组的23.33%(P <0.05)。结论静脉肾盂造影患者采用指压天枢穴加腹部按摩联合番泻叶的效果较好,可提高肠道清洁力度,降低不良反应发生率。     

急性肺血栓栓塞的急诊诊疗研究进展

急性肺血栓栓塞(APTE)是内科的危急重症之一,是以肺部功能障碍及右心室功能不全为主要特征的心血管疾病。其临床表现复杂,但又缺乏特异性表现,因而易与其他疾病相混淆,造成误诊、漏诊。近年来,APTE的发病率以及致死率呈逐年上升的趋势,使患者的生命健康受到严重威胁。该文系统总结了APTE的诱发因素以及病理学特点,详细介绍了实验室检查以及常用的影像学检查等先进手段,并针对APTE的紧急处理以及当前主要的溶栓和抗凝治疗方法进行概述。     

3M无痛保护膜联合造口袋治疗大便失禁相关性皮炎的临床观察

目的探讨用3M无痛保护膜联合造口袋治疗大便失禁相关性皮炎(Incontinent dermatitis,IAD)的疗效。方法选择我院2014年12月至2015年6月收治的50例IAD患者为研究对象,按照随机数表法将其分为对照组与实验组,每组25例。对照组患者予以传统皮肤护理方法;实验组的患者除传统皮肤护理外加用3M无痛保护膜联合造口袋治疗。并比较两组患者治疗后的皮炎改善情况、皮炎愈合时间及皮炎复发率。结果实验组患者治疗效果明显优于对照组,实验组皮炎愈合短于对照组,实验组皮炎复发率明显低于对照组,差异均有统计学意义(P<0.05)。结论 3M无痛保护膜联合造口袋治疗IAD,临床效果显著,且愈合快,复发低。