Opposing patterns in self-reported and measured physical activity levels in middle-aged adults

Physical activity brings significant health benefits to middle-aged adults, although the research to date has been focused on late adulthood. This study aims to examine how ageing affects the self-reported and accelerometer-derived measures of physical activity levels in middle-aged adults. We employed the data recorded in the UK Biobank and analysed the physical activity levels of 2,998 participants (1381 men and 1617 women), based on self-completion questionnaire and accelerometry measurement of physical activity. We also assessed the musculoskeletal health of the participants using the dual-energy X-ray absorptiometry (DXA) measurements provided by the UK Biobank. Participants were categorised into three groups according to their age: group I younger middle-aged (40 to 49 years), group II older middle-aged (50 to 59 years), and group III oldest middle-aged (60 to 69 years). Self-reported physical activity level increased with age and was the highest in group III, followed by group II and I (P?<?0.05). On the contrary, physical activity measured by accelerometry decreased significantly with age from group I to III (P?<?0.05), and the same pertained to the measurements of musculoskeletal health (P?<?0.05). It was also shown that middle-aged adults mostly engaged in low and moderate intensity activities. The opposing trends of the self-reported and measured physical activity levels may suggest that middle-aged adults over-report their activity level as they age. They should be aware of the difference between their perceived and actual physical activity levels, and objective measures would be useful to prevent the decline in musculoskeletal health.

     

信息化技术应用于静脉血栓栓塞症护理的研究进展

综述信息化技术在静脉血栓栓塞症护理领域中风险预测、警报接收与上报、抗凝防治管理、医护人员相关继续医学教育、患者疾病预后管理的应用现状，总结其应用阻碍因素，旨在为我国静脉血栓栓塞症护理信息化建设提供参考。     

Correction: Thrombotic and bleeding events,mortality, and anticoagulant use among 546,656 hospitalized patients with COVID‑19 in the United States: a retrospective cohort study

Journal of Thrombosis and Thrombolysis -     

中国抗癌协会神经内分泌肿瘤诊治指南（2022年版）

神经内分泌肿瘤（neuroendocrine neoplasm,NEN）是一类起源于肽能神经元和神经内分泌细胞,具有神经内分泌分化并表达神经内分泌标志物的少见肿瘤,可发生于全身各处,以肺及胃肠胰NEN（gastroenteropancreatic neuroendocrine neoplasm, GEP-NEN）最常见。国内外研究数据均提示,NEN的发病率在不断上升。美国流行病学调查结果显示,与其他类型肿瘤相比,NEN的发病率上升趋势更为显著。中国抗癌协会神经内分泌肿瘤专委会在现有循证医学证据基础上,结合已有国内外指南和共识,制订了首版中国抗癌协会神经内分泌肿瘤诊治指南,为临床工作者提供参考。     

物联网技术在医院中的应用现状

物联网(Internet of Things,简称"IoT")技术是当前世界新一轮科技和经济发展的战略制高点之一。物联网是由传感技术感知物体,按照固定的协议实现任何时候物与物之间、人和物之间、人与人之间的互联互通,实现智能化识别、定位、跟踪及管理的网络,是信息技术和传感控制技术两者融合的产物。简单来说,物联网就是可以让人们所关注的事物在任何地方、任何时候都能被监控,其具有全面感知、可靠传递以及智能处理3个特点。物联网的核心是智能化,其在医院中的应用主要是实现对人的智能化医疗和对物的智能化管理工作,例如:实现婴儿防盗、无线输液管理、移动门诊输液管理等功能;用于医疗资产管理、资产定位,提升医疗器械调度效率。     

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.     

解剖锁定钢板联合同种异体腓骨治疗肱骨近端骨折疗效的Meta分析

文题释义：肱骨近端骨折：肱骨近端包括肱骨头及大结节、小结节，中老年人骨质疏松及低能量损伤可导致肱骨近端骨折。 同种异体腓骨：取自于人体异体，经过加工处理，去除其免疫原性，保留其骨性结构，可用于移植修复骨缺损，起到支撑作用。 背景：肱骨近端骨折是临床常见骨折，但对肱骨近端内侧柱缺乏支撑的骨折目前仍是治疗难点，并发症常见，失败率较高。 目的：比较解剖锁定钢板联合同种异体腓骨与单纯解剖锁定钢板治疗肱骨近端骨折的疗效。 方法：使用计算机检索PubMed、Embase、Cochrane Library、Google Scholar、中国知网、万方、维普数据库，检索时间均从建库到2020年2月。检索国内外关于对比研究解剖锁定钢板联合同种异体腓骨与单纯解剖锁定钢板治疗肱骨近端骨折疗效的文献。2名研究员根据纳入和排除标准分别独立筛选文献，提取数据，评估文献中的偏倚风险。纳入12篇相关文献使用RevMan 5.2软件将以下指标进行Meta分析，包括影像学数据、功能评分和并发症。结果与结论：①通过文献检索、根据纳入和排除标准，12篇文献纳入研究，其中11篇为回顾性队列研究，1篇为随机对照研究；纳入研究文献质量高，但GRADE证据质量级别较低。②共纳入958例患者，其中解剖锁定钢板联合同种异体腓骨组411例，单纯解剖锁定钢板组547例；③Meta分析结果显示，解剖锁定钢板联合同种异体腓骨组术后1年肱骨头高度差值(MD=-2.40，95%CI：-2.49至-2.31)、颈干角差值(MD= -6.14，95%CI：-6.62至-5.67)、目测类比评分(MD=-0.22，95%CI：-0.35至-0.08)、肩关节功能评分(MD=4.12，95%CI：2.18-6.06)，上肢伤残评分(MD=-10.32，95%CI：-13.44至-7.19)、术后2年的目测类比评分(MD=-0.37，95%CI：-0.55至-0.19)、肩关节功能评分(MD=5.07，95%CI：2.86-7.27)、总体并发症(OR=0.31，95%CI：0.20-0.48)及肱骨头螺钉切出(OR=0.25，95%CI：0.11-0.55)均明显优于单纯解剖锁定钢板组(P < 0.05)，肱骨头坏死(OR=0.94，95%CI：0.47-1.88)，两组间差异无显著性意义(P > 0.05)；④因此，较弱的证据提示，肱骨近端解剖锁定钢板联合同种异体腓骨治疗肱骨近端骨折的短期疗效优于解剖锁定钢板，可减少并发症的发生，促进功能恢复。ORCID: 0000-0002-8486-3932(阳运康) 中国组织工程研究杂志出版内容重点：人工关节；骨植入物；脊柱；骨折；内固定；数字化骨科；组织工程