Simeprevir added to peginterferon and ribavirin lessens time with fatigue,depressive symptoms and functional limitations in patients with chronic hepatitis C compared with peginterferon and ribavirin: results from 1161 patients in the QUEST‐1, QUEST‐2 and PROMISE studies

The value of adding simeprevir (SMV) vs placebo (PBO) to peginterferon and ribavirin (PR) for treatment of chronic hepatitis C virus infection was examined using patient‐reported outcomes (PROs); further, concordance of PROs with virology endpoints and adverse events (AEs) was explored. Patients (n = 768 SMV/PR, n = 393 PBO/PR) rated fatigue (FSS), depressive symptoms (CES‐D) and functional impairment (WPAI: Hepatitis C Productivity, Daily Activity and Absenteeism) at baseline and throughout treatment in three randomised, double‐blind trials comparing the addition of SMV or PBO during initial 12 weeks of PR. PR was administered for 48 weeks (PBO group) and 24/48 weeks (SMV group) using a response‐guided therapy (RGT) approach. Mean PRO scores (except Absenteeism) worsened from baseline to Week 4 to the same extent in both groups but reverted after Week 24 for SMV/PR and only after Week 48 for PBO/PR. Accordingly, there was a significantly lower area under the curve (baseline–Week 60, AUC₆₀) and fewer weeks with clinically important worsening of scores in the SMV/PR group at any time point. Incidences of patients with fatigue and anaemia AEs were similar in both groups, but FSS scores showed that clinically important increases in fatigue lasted a mean of 6.9 weeks longer with PBO/PR (P < 0.001). PRO score subgroup analysis indicated better outcomes for patients who met the criteria for RGT or achieved sustained virological response 12 weeks post‐treatment (SVR12); differences in mean PRO scores associated with fibrosis level were only observed with PBO/PR. Greater efficacy of SMV/PR enabled reduced treatment duration and reduced time with PR‐related AEs without adding to AE severity.     

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.     

改良三点式重睑成形术的临床应用研究

目的:探讨改良三点式重睑术的临床应用疗效。方法:2012年6月-2019年6月,共165例单睑患者采用了改良三点式重睑术,沿术前标记线将三点处皮肤切开,剪刀将切口下唇的眼轮匝肌适当去除,同时将切口与切口在皮下层打通,剪除切口与切口之间的眼轮匝肌,6-0可吸收线挂睑板前筋膜或提上睑肌腱膜及切口下唇皮下组织缝合,三点切口各缝1针。再用6-0单丝尼龙线按照常规重睑线缝合方法挂切口下唇皮肤、睑板前筋膜或提上睑肌腱膜及切口上唇皮肤缝合打结,三点切口各缝1针。伴内眦赘皮者同时行内眦赘皮矫正术。结果:152例患者获得随访,随访患者大部分获得了比较满意的重睑,睁眼重睑流畅、自然,闭眼刀口痕迹不明显。2例患者出现内侧重睑线变浅,1例患者出现外侧重睑线变浅,所有患者均未出现重睑消失。5例患者双侧重睑线有轻度不对称。患者总体满意率为94.7%(144/152)。结论:改良三点式重睑术具有创伤小、并发症少、效果逼真、不易脱落、手术痕迹不明显等优点,值得推广应用。     

Skin closure with 2-octyl cyanoacrylate and polyester mesh after primary total knee arthroplasty offers superior cosmetic outcomes and patient satisfaction compared to staples: a prospective trial

European Journal of Orthopaedic Surgery & Traumatology - The goals of this study were to compare patient satisfaction and wound-related complications in patients receiving 2-octyl cyanoacrylate...     

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。