白花蛇舌草干预湿热瘀滞型肠内多发息肉术后复发的临床研究

目的 临床观察白花蛇舌草干预湿热瘀滞型肠内多发息肉患者内镜治疗术后复发情况。方法 采用前瞻性的随机空白对照试验设计，选取2019年9月1日—2020年12月31日上海中医药大学附属普陀医院消化科收治的门诊及住院结直肠多发息肉患者132例作为研究对象。采用随机方法分为对照组和试验组，每组66例。对照组行内镜治疗术给予常规治疗后无药物干预，试验组在对照基础上给予白花蛇舌草汤剂（每日取白花蛇舌草15 g、大枣3枚，煎取400 mL汤剂，分2次饭后温服）治疗，连续干预1年。观察并比较两组术前与术后1年肠道内息肉的复发率、息肉数目、息肉最大直径、中医证候评分及肝肾功能及血常规的差异。结果 干预1年后，试验组复发9例（14.75%），对照组复发21例（33.33%），两组息肉复发率比较，差异有统计学意义（P＜0.05）。试验组腺瘤、伴重度异型增生、体质量指数（BMI）≥24 kg·m^-2患者治疗后息肉复发率均较对照组同类型降低，差异显著（P＜0.05）；术后1年试验组息肉最大直径及息肉数目均较对照组显著减小，差异显著（P＜0.05）；术后1年试验组中医证候各项评分均显著低于对照组（P＜0.05）；术后1年，两组中医证候疗效比较，差异具有统计学意义（P＜0.05），且术前、术后1年两组患者肝肾功能、凝血功能等安全性指标无显著差异。结论 应用白花蛇舌草干预结直肠息肉术后患者，1年后明显降低肠内息肉的复发率，尤以腺瘤性息肉、重度异型增生、BMI超重患者更显著，不仅改善临床症状，还能有效预防结直肠息肉的复发，改善患者的预后。     

不同非密闭雾化治疗方式在不合作患儿中的应用效果

目的 探讨不同非密闭雾化方式在不合作患儿接受布地奈德雾化治疗中的应用效果。方法 选取本院门诊270例3岁以下使用布地奈德雾化治疗的患儿,采用随机数字表法将患儿分为Pari雾化系统、Philips雾化系统及Salter雾化系统3个治疗组,毎组90例,各组又依据面罩距离面部0 cm、2 cm、4 cm分为3个小组,毎组30例,分别测量各组雾化气流流速及吸入雾化颗粒量占总雾化颗粒量的比例。采用方差分析及Bonferroni进行统计分析。结果 雾化气流流速及雾化吸入量在不同雾化系统组间差异有统计学意义(P<0.05)。在3种距离下,Pari系统均能使患儿吸入最多量的雾化颗粒,其次是Salter系统,再是Philips系统,与此相反,气流流速Pari系统最低。Pari系统离开面部4 cm及Salter系统离开面部2 cm的吸入量均高于Philips系统0 cm的吸入量。结论 选择合适的非密闭雾化系统方式,是提高不合作患儿布地奈德雾化治疗的有效方式。     

糖尿病黄斑水肿患者病变程度与脉络膜厚度的关系

目的　利用频域光学相干断层扫描深度增强（enhanced depth imaging spectral domain optical coherence tomography，EDI SD-OCT）观察糖尿病黄斑水肿（diabetic macular edema，DME）患者脉络膜厚度（choroidal thickness，CT）的变化及结构特点，探讨DME病变程度与CT的关系。方法　纳入2型糖尿病患者共123例204眼，其中69眼诊断为DME（DME组），135眼无黄斑水肿为对照组。DME眼依据OCT形态学特点进一步分为视网膜弥漫性增厚（diffuse retinal thickness，DRT）型（34眼）、黄斑囊样水肿（cystoid macular edema，CME）型（19眼）和浆液性视网膜脱离（serous retinal detachment，SRD）型（16眼），利用EDI-OCT分别测量黄斑中心凹下CT和以黄斑为中心上、下、鼻、颞500 μm、1000 μm、1500 μm、2000 μm处CT。结果　DME组黄斑中心凹下CT为（326.72±90.15）μm，对照组为（320.17±106.46）μm，两组之间无统计学差异，但黄斑中心凹下CT与视网膜厚度间具有明显正相关关系（r=0.270，P=0.025）。DME亚型CT分别为:DRT型（303.94±81.47）μm、CME型（304.42±73.98）μm和SRD型（401.63±88.80）μm，SRD型CT明显高于其他亚型（P<0.05），此外，SRD型的周边CT同样呈现均匀一致的增厚；鼻侧CT从500 μm至2000 μm呈距离敏感性降低（P<0.05），但SRD型鼻侧CT降低幅度明显变缓（P=0.195）。结论　SRD型黄斑水肿患者CT在中心凹下及周边部均显著增厚，CT与DME病变程度之间有一定相关性。     

多囊卵巢综合征妊娠患者与非多囊卵巢综合征孕妇妊娠结局比较

目的比较多囊卵巢综合征(PCOS)妊娠患者与非PCOS孕妇的妊娠结局。方法选取200例PCOS妊娠患者作为研究组,并选取同期200例非PCOS孕妇作为对照组,比较两组孕妇的妊娠结局、新生儿情况及妊娠期并发症情况。结果研究组的先兆流产、早产、足月剖宫产发生率显著高于对照组(P <0.05);两组的先兆早产发生率比较无统计学差异(P>0.05)。两组的新生儿情况比较无统计学差异(P>0.05)。研究组的妊娠期高血压综合征、妊娠期糖尿病、羊水异常、胎儿窘迫发生率显著高于对照组(P <0.05)。结论与非PCOS孕妇相比,PCOS妊娠患者的早产风险增加,且妊娠期高血压综合征、妊娠期糖尿病、羊水异常、胎儿窘迫发生率显著升高。     

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.     

经9次核酸检测终确诊的新型冠状病毒肺炎患者1例分析

目前新型冠状病毒肺炎(COVID-19)疫情在全球蔓延,形势严峻,并对全球公共卫生事业构成巨大挑战,快速准确的诊断COVID-19对疫情的防控有重要意义。本文报道1例COVID-19患者的调查和确诊过程,以对COVID-19确诊和疫情防控提供有价值的参考。患者为赴渝务工于2020年1月23日返乡人员,在渝期间,其同工地的一同事确诊为COVID-19,之后该工地接连有另外8位同事被确诊。患者返乡后2月2日被当地疫情防控单位采取医学隔离。患者于隔离后不久出现咳嗽,阵发性,干咳为主,偶可咳出黄色痰液等COVID-19相关临床症状,遂被当地新型冠状病毒肺炎定点医疗单位收治,后经市级新型冠状病毒肺炎诊治专家组远程视频会诊转入四川大学华西医院资阳医院。患者先后经历9次核酸检测,在前8次核酸检测均为阴性的情况下,于第9次核酸检测结果为阳性,2月21日最终确诊为新型冠状病毒肺炎。     

门诊和病房电脑鼠标细菌污染情况调查

目的调查门诊和普通病房电脑鼠标不同时段的细菌污染情况。方法随机抽取某院门诊及普通病房的各16个电脑鼠标，分别于8∶00、10∶30、13∶00、15∶30进行采样，样本接种培养后对菌落计数并筛查多重耐药菌。结果32个鼠标共计采样128次，最高菌落数达121 CFU/cm2，平均菌落数为23.9 CFU/cm2。随着时间的推移，同一鼠标的细菌计数逐渐增多。不同时段门诊和病房的电脑鼠标菌落数比较，在10∶30、13∶00、15∶30三个时间点，病房鼠标的菌落数均高于门诊（均P≤0.05）。64个病房电脑鼠标标本中检出耐甲氧西林金黄色葡萄球菌（MRSA）和耐碳青霉烯类鲍曼不动杆菌（CRAB）各1株。结论加强鼠标日常清洁消毒及监测工作非常重要，应增加清洁消毒频率或更换长效消毒剂，加强病房环境的清洁消毒。     

品管圈活动在喜疗妥联合新鲜马铃薯外敷治疗小儿输液外渗中的应用效果分析

目的探讨品管圈活动在喜疗妥联合新鲜马铃薯外敷治疗小儿输液外渗中的应用效果。方法选取我院2016年8月至2018年12月收治的输液外渗患儿100例,随机分为两组各50例。对照组给予50%硫酸镁湿敷治疗与常规护理,干预组采用喜疗妥联合新鲜马铃薯外敷,进行品管圈活动。对比两组的临床疗效、损伤痊愈时间和完全消肿时间。结果干预组的总有效率为90.00%,高于对照组的74.00%(P <0.05)。干预组的损伤痊愈时间和完全消肿时间短于对照组(P <0.05)。结论品管圈活动在喜疗妥联合新鲜马铃薯外敷治疗小儿输液外渗中的效果较好,可缩短临床症状消失时间,加快康复进程。