我国茯苓炮制加工和产品研发现状与展望

茯苓是我国大宗中药原料,也是卫生部批准的药食两用中药资源。趁鲜蒸制加工已基本取代"发汗"工艺,成为茯苓主流的初加工技术。茯苓经不同方法炮制得到有白茯苓、赤茯苓、茯苓皮、炒茯苓和朱茯苓,其功效各有侧重。茯苓在古代经典名方中应用广泛,现代已形成桂枝茯苓丸等主打中成药产品。国家中医药管理局公布的《古代经典名方目录(第一批)》100首方剂中,以茯苓为主要原料的经典名方占到了24%。以茯苓为原料的保健食品多达776个,主要保健功能为免疫调节、缓解体力疲劳和改善睡眠。茯苓的应用已经形成了涉及普通食品、化妆品、中兽药和饲料添加剂的综合开发体系。加强质量控制、规范化炮制生产和功效突出的产品研发将是茯苓产业发展的趋势。     

腹腔镜胆囊切除术治疗急性结石性胆囊炎的疗效

目的探讨分析对急性结石性胆囊炎患者采用腹腔镜胆囊切除术进行治疗的临床效果,以及对患者的胃肠功能和C反应蛋白所造成的影响。方法本次研究对象乃是我院肝胆外科于2017年4月-2019年4月期间收治的急性结石性胆囊炎患者62例,按照患者就诊的先后顺序对其进行平均分组,比较两组患者的术后肠鸣音恢复时间、肛门排气时间、排便时间、C反应蛋白水平以及并发症发生率。结果腹腔镜手术组患者的术后肠鸣音恢复时间(13.6±3.5)小时、肛门排气时间(16.5±2.7)小时以及排便时间(25.7±3.3)小时,均明显少于开腹式手术组患者(P<0.05);腹腔镜手术组患者的术后并发症发生率(6.45%)明显低于开腹式手术组患者(25.80%)(P<0.05);腹腔镜手术组患者的C反应蛋白水平为(10.4±2.5)mg/L,少于开腹式手术组患者(P<0.05)。结论根据本次研究的结果可以确认,对急性结石性胆囊炎患者采用腹腔镜胆囊切除术进行治疗能够取得更好的效果,可以促使患者的胃肠功能在术后更快的恢复,提高患者的C反应蛋白,从而有效避免患者出现并发症。     

高糖饮食对小鼠真菌性角膜炎的影响

目的　探究高糖饮食对小鼠真菌性角膜炎的影响。方法　选取健康无眼疾的雄性C57BL/6J小鼠78只，随机分为高糖饮食组和模型对照组，每组36只，模型对照组给予正常饮用水，高糖饮食组给予含体积分数10%果糖溶液，每2 d测量两组小鼠体质量及血糖，10 d后建立真菌性角膜炎模型。造模后24 h、36 h、48 h、72 h、96 h、120 h、168 h裂隙灯显微镜下对角膜进行临床评分并拍照。处死小鼠后，取角膜组织进行HE染色和PAS染色；测定角膜内中性粒细胞和巨噬细胞浸润体积。利用酶联免疫吸附实验对小鼠角膜内的白细胞介素-1β含量进行测定。结果　造模后 0~14 d，两组小鼠体质量与血糖差异均无统计学意义（均为P>0.05）。造模后24 h、36 h、48 h、120 h、168 h，高糖饮食组小鼠角膜临床评分均明显高于模型对照组，差异均有统计学意义（均为P<0.05）。高糖饮食组小鼠角膜穿孔率79.5%，高于模型对照组的40.9%，差异有统计学意义（P=0.000）。造模后各时间点，高糖饮食组中性粒细胞浸润体积均高于模型对照组，差异均有统计学意义（均为P=0.000）。造模后72 h、96 h、120 h、168 h，高糖饮食组巨噬细胞浸润体积均高于模型对照组（均为P=0.000）。角膜组织病理学检查结果示，高糖饮食组炎症反应更重，角膜组织破坏更早且更为严重。造模后24 h、48 h高糖饮食组白细胞介素-1β含量均明显高于模型对照组（均为P<0.05）。结论　高糖饮食加重了真菌性角膜炎感染的严重程度，增强了中性粒细胞、巨噬细胞的趋化，促进了IL-1β的分泌。     

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.     

放射治疗记录与验证系统质量控制指南

放疗记录与验证系统(RVS)是一套用于防止医用电子加速器等放疗设备治疗参数设置错误,并且记录所有放疗阶段执行参数的医用计算机软件控制系统。为确保患者的治疗安全,必须对记录与验证系统采取必要的质量控制措施。本指南内容涉及:RVS安装和参数设定过程中的质量控制;RVS的验收测试;RVS在临床使用过程中的持续质量控制;使用RVS过程中的典型错误类型;执行RVS验收测试的具体测试例。     

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。     

Pathophysiology and management of sensitive skin: position paper from the special interest group on sensitive skin of the International Forum for the Study of Itch (IFSI)

The special interest group on sensitive skin of the International Forum for the Study of Itch previously defined sensitive skin as a syndrome defined by the occurrence of unpleasant sensations (stinging, burning, pain, pruritus and tingling sensations) in response to stimuli that normally should not provoke such sensations. This additional paper focuses on the pathophysiology and the management of sensitive skin. Sensitive skin is not an immunological disorder but is related to alterations of the skin nervous system. Skin barrier abnormalities are frequently associated, but there is no cause and direct relationship. Further studies are needed to better understand the pathophysiology of sensitive skin – as well as the inducing factors. Avoidance of possible triggering factors and the use of well-tolerated cosmetics, especially those containing inhibitors of unpleasant sensations, might be suggested for patients with sensitive skin. The role of psychosocial factors, such as stress or negative expectations, might be relevant for subgroups of patients. To date, there is no clinical trial supporting the use of topical or systemic drugs in sensitive skin. The published data are not sufficient to reach a consensus on sensitive skin management. In general, patients with sensitive skin require a personalized approach, taking into account various biomedical, neural and psychosocial factors affecting sensitive skin.