生命早期抗生素使用与儿童哮喘:因果关系还是混杂效应?

儿童哮喘的发病率逐年上升.基于卫生假说,抗生素使用可能减少了微生物暴露,从而增加了过敏性疾病发生的风险.近十年来,就早期抗生素暴露与儿童哮喘的关系进行的大量的流行病学调查的结果并不一致.大多数回顾性研究发现正相关联系,但前瞻性研究未发现联系或联系强度较弱.逆向因果和指示混淆可部分解释两者的关系,但也难以否定因果关系的存在.     

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.     

Mixed Reality Combined with Three‐Dimensional Printing Technology in Total Hip Arthroplasty: An Updated Review with a Preliminary Case Presentation

Three‐dimensional (3D) printing technology, virtual reality, and augmented reality technology have been used to help surgeons to complete complex total hip arthroplasty, while their respective shortcomings limit their further application. With the development of technology, mixed reality (MR) technology has been applied to improve the success rate of complicated hip arthroplasty because of its unique advantages. We presented a case of a 59‐year‐old man with an intertrochanteric fracture in the left femur, who had received a prior left hip fusion. After admission to our hospital, a left total hip arthroplasty was performed on the patient using a combination of MR technology and 3D printing technology. Before surgery, 3D reconstruction of a certain bony landmark exposed in the surgical area was first performed. Then a veneer part was designed according to the bony landmark and connected to a reference registration landmark outside the body through a connecting rod. After that, the series of parts were made into a holistic reference registration instrument using 3D printing technology, and the patient's data for bone and surrounding tissue, along with digital 3D information of the reference registration instrument, were imported into the head‐mounted display (HMD). During the operation, the disinfected reference registration instrument was installed on the selected bony landmark, and then the automatic real‐time registration was realized by HMD through recognizing the registration landmark on the reference registration instrument, whereby the patient's virtual bone and other anatomical structures were quickly and accurately superimposed on the real body of the patient. To the best of our knowledge, this is the first report to use MR combined with 3D printing technology in total hip arthroplasty.