Discordant Electrocardiogram Left Ventricular Wall Thickness and Strain Findings in Influenza Myocarditis

A 42‐year‐old man presented with a viral prodrome and tested positive for influenza A. He rapidly deteriorated developing cardiogenic shock, rhabdomyolysis, and acute kidney injury. Patient improved 1 week later with supportive measures including vasopressors, inotropes, and an intraaortic balloon pump. We report this case as it highlights the discordance between echocardiographic ventricular wall thickening as a result of myocardial edema, and electrocardiographic findings at presentation, with a reversal in findings at time of resolution. Additionally, there was some suggestion of a regional pattern to the reduced longitudinal strain.     

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.     

血清胱抑素C和尿微量白蛋白联合检测在人类免疫缺陷病毒感染者肾功能损伤检测中的应用价值

目的评估血肌酐和尿常规的常规检测基础上联合血清胱抑素C和尿微量白蛋白检测在人类免疫缺陷病毒（HIV）感染者肾功能损伤检测中的应用价值。 方法以2019年2～5月于首都医科大学附属北京地坛医院感染一科住院的169例HIV感染者为研究对象，完善其尿常规、尿微量白蛋白、血肌酐、血清胱抑素C检测；分析尿蛋白及尿微量白蛋白的阳性检出率及其差异，血肌酐升高及血清胱抑素C升高的比例及其差异。计算应用替诺福韦酯（TDF）及合并丙型肝炎、高血压、糖尿病、肿瘤的肾功能损伤的相对危险度。 结果169例HIV感染者中尿常规示尿蛋白阳性者5例（3.0%），尿微量白蛋白升高者17例（10.1%），两者阳性检出率差异具有统计学意义（χ² = 5.9、P = 0.007）。血肌酐升高者10例（5.9%），血清胱抑素C升高者20例（11.8%），两者阳性检出率差异具有统计学意义（χ² = 3.0、P = 0.042）。在尿常规和血肌酐检测的基础上联合检测尿微量白蛋白和血清胱抑素C的总体阳性检出率为49例（30.0%）。CD4⁺ T淋巴细胞计数< 200个/μl与≥ 200个/μl组患者血清胱抑素C水平分别为0.94（0.83，1.05）mg/L、0.85（0.77，0.95）mg/L，差异具有统计学意义（Z =-3.02、P = 0.003）。应用TDF及合并丙型肝炎、高血压、糖尿病、肿瘤的肾功能损伤的相对危险度分别为1.1、1.5、1.9、2.2和1.4。 结论HIV感染者中，单纯以尿常规为依据评估有无蛋白尿，以血肌酐水平为依据评估肾小球滤过功能会低估肾功能损伤的患病率。在常规检测血肌酐和尿常规的基础上联合检测血清胱抑素C和尿微量白蛋白在提高HIV感染者肾功能损伤检出率方面具有重要的应用价值。低CD4⁺ T淋巴细胞计数、应用TDF及合并丙型肝炎、高血压、糖尿病、肿瘤均为肾功能损伤的危险因素。     

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...