靶向测序诊断黑棘皮病1家系

【摘要】 报道临床症状不典型的家族性黑棘皮病1家系。先证者女，4岁，自1周岁时，颈部、腹部出现黑色斑片，近年来逐渐扩大至唇周、躯干前部。腹部皮肤全反式共聚焦显微镜检查可见乳头环下延扭曲及沟壑结构，乳头环内可见中高折光颗粒结构。先证者父亲及祖母既往有类似病史，但随着年龄增长色素沉着自发性消退，仅有局部皮纹增粗。采集先证者及父母、祖母外周血，对先证者外周血DNA行Panel靶向测序，结果显示，先证者存在FGFR3基因14号外显子c.1949A>C（p.Lys650Thr）错义突变，Sanger测序验证证实先证者及其父亲和祖母均存在此突变。诊断：家族性黑棘皮病。     

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.     

细胞力学在压力性损伤中的作用及影响因素研究进展

随着压力性损伤的研究重心逐渐向细胞水平转移，发现力学因素下细胞结构和功能改变在压力性损伤发展过程中起重要作用。综述力学因素下压力性损伤发生机制、影响因素、评估以及预防管理。旨在提高护理人员对压力性损伤更为深入地认知，从而推动压力性损伤研究多元化发展。     

2019年重庆市30岁及以上人群归因于被动吸烟的肺癌疾病负担研究

目的 分析重庆市肺癌发病死亡和疾病负担归因于被动吸烟的情况,为开展肺癌防治提供建议。 方法 肺癌死亡个案数据来源于2019年重庆市肿瘤登记报告系统,被动吸烟率来自2013年重庆市慢性病及危险因素监测。计算人群归因危险度百分比(population attributable risk percent, PAR%)、被动吸烟导致的肺癌发病、死亡和疾病负担。采用Excel 2010与SPSS 25.0进行统计分析,率的比较采用χ²检验。 结果 2013年30岁及以上成年人被动吸烟率为52.37%。2019年重庆市30岁及以上人群肺癌发病率与标化发病率分别为118.44/10万与80.83/10万,死亡率与标化死亡率分别为96.51/10万、63.58/10万。肺癌发病率和死亡率归因于被动吸烟的PAR%分别为19.76和19.04,归因发病率与归因标化发病率分别为23.41/10万和16.34/10万,归因死亡率与归因标化死亡率分别为18.38/10万和12.40/10万。2019年重庆市30岁及以上肺癌早死所致寿命损失年率(years of life lost,YLL)、残疾所致寿命损失年率(years lived with disability,YLD)、调整伤残寿命损失年率(disability adjusted life year,DALY)分别为21.16‰、0.31‰、21.47‰,YLL率、YLD率、DALY率归因于被动吸烟的PAR%分别为21.16、19.76和20.49,归因YLL率为4.34‰,归因YLD率为0.06‰,归因DALY率为4.40‰。 结论 2019年重庆市30岁及以上人群肺癌发病率、死亡率、YLL率、DALY率高,被动吸烟率高,肺癌归因于被动吸烟的疾病负担重,应加强落实控烟工作。     

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.