The home environment in a nationwide sample of multi-family buildings in Sweden: associations with ocular,nasal, throat and dermal symptoms,headache, and fatigue among adults

Risk factors at home for ocular, nasal, throat and dermal symptoms, headache, and fatigue were studied in a nationwide questionnaire survey in Sweden, the BETSI study in 2006. Totally, 5775 adults from a stratified random sample of multi-family buildings participated. Associations between home environment factors and weekly symptoms were analyzed by multi-level logistic regression. In total, 8.3% had ocular symptoms; 11.9% nasal symptoms; 7.1% throat symptoms; 11.9% dermal symptoms; 8.5% headache and 23.1% fatigue. Subjects in colder climate zones had more mucosal and throat symptoms but less fatigue and ocular symptoms. Rented apartments had poorer indoor environment than self-owned apartments. Those living in buildings constructed from 1961 to 1985 had most symptoms. Building dampness, mold and mold odor were risk factors, especially headache and ocular symptoms. Lack of mechanical ventilation system was another risk factor, especially for headache. Environmental tobacco smoke (ETS), electric radiators, and crowdedness were other risk factors. Oiled wooden floors, recent indoor painting, and new floor materials were negatively associated with symptoms. In conclusion, building dampness, mold, poor ventilation conditions, crowdedness, ETS, and emissions from electric radiators in apartments in Sweden can increase the risk of ocular, nasal, throat and dermal symptoms, headache, and fatigue.     

Sparse non-convex Lp regularization for cone-beam X-ray luminescence computed tomography

Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, and it has the potential of monitoring the metabolic processes of nanophosphors-based drugs in vivo. However, the XLCT imaging suffers from a severe ill-posed problem. In this work, a sparse nonconvex L_p (0?p?L₁ regularization. Further, an iteratively reweighted split augmented lagrangian shrinkage algorithm (IRW_SALSA-L_p) was proposed to efficiently solve the non-convex L_p (0?p?p-values (1/16, 1/8, 1/4, 3/8, 1/2, 5/8, 3/4, 7/8) in both 3D digital mouse experiments and in vivo experiments. The results demonstrate that the proposed non-convex methods outperform L₂ and L₁ regularization in accurately recovering sparse targets in CB-XLCT. And among all the non-convex p-values, our L_p(1/4?p?相似文献   

Reproducible,biocompatible medical materials from biologically derived polysaccharides: Processing and Characterization

Natural polysaccharides like chitosan and dextran have garnered considerable interest in biomedical applications due to their biocompatibility, biodegradability, and nontoxicity. Nonetheless, the development of a reproducible class of medical devices from these materials is challenging and has had limited success. Chitosan and dextran are inherently variable and synthesis using these materials is prone to inconsistencies. In this study, we put forward a robust product development regimen that allows these natural materials to be developed into a reproducible class of biomaterials. First, an array of validated characterization methods (Proton Nuclear Magnetic Resonance, titrations, Ultraviolet spectroscopy, Size Exclusion Chromatography—Multi-Angle Light Scattering, Size Exclusion Chromatography—Refractive Index, and proprietary methods) were developed that allowed rigorous specifications to be set for unprocessed chitosan and dextran, chitosan and dextran intermediates, and chemically modified materials—acrylated chitosan (aCHN) and oxidized dextran (oDEX). Second, a robust and reproducible synthesis scheme involving various in-process controls was developed to chemically modify the unprocessed polysaccharides. Third, purification methods to remove byproducts and low-molecular-weight impurities for both aCHN and oDEX were developed. The study presents a viable strategy for converting variable, natural materials into a reproducible class of biomaterials that can be applied in various biomedical applications. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48454.     

Using a hybrid two‐stage DEA model to analyze the profitability and marketability of medical manufacturing firms

The purpose of this study is to develop a modification of the model developed by Chen and Zhu in 2004. Calculating stage and overall efficiencies precisely and consistently has become a major challenge of the two‐stage DEA model. However, most other models do not calculate the optimality of intermediates. Although the model developed by Chen and Zhu measures the optimality of intermediates, the calculated efficiency scores still have some shortfalls. The modified model, named the hybrid two‐stage DEA model, fills the gap between calculating the optimality of intermediates and the consistency of overall efficiency scores. In addition to obtaining an accurate measurement for the optimality of intermediates, the model confines efficiency scores to a range from zero to one (a ratio efficiency score). In an empirical evaluation, we use data from 64 medical manufacturing firms to test the performance of the hybrid model and offer recommendations for the industry.     

重庆市药物临床试验机构现状分析及地区性机构体系构建的思考

目的：重庆市具有丰富的医药产业底蕴和极佳的医疗资源，在我国药品注册审评制度改革紧密推进和重庆市大力发展生物医药产业的大背景下，本课题从对重庆市药物临床试验机构（以下简称机构）管理人员调研的角度入手，旨在明确重庆市机构建设现状与问题，探讨可行的创新发展对策。方法：本研究对我市20家有药物临床试验资质机构的54位管理人员问卷数据进行统计；并通过比对相应的公共数据，系统分析我市机构建设水平。结果：问卷分析显示，我市机构仍以传统GCP架构模式为主；以承担国内企业项目为主（90.00%），65.00%的机构近三年承接项目总数在40项以下；机构管理者多数从事机构相关工作的工作年限多在6年以内（79.59%），且仍有部分机构管理者未接受系统的培训；对公共数据调查分析显示，截至2019年5月底，我市机构总数目为22家，自2017年7月22日以来承接在Clinical Trails注册的在研项目数为81项，远低于机构工作开展较好的城市（北京：61家，433项；上海：53家，326项）。结论：重庆市机构建设水平亟待提高，应整合全市临床研究资源，构建区域化的药物临床试验机构体系，加速重庆市医药产业的创新发展。     

一次性使用医用手套标准解读

一次性医用手套是能够防止人们交叉感染的常用防护用品。随着新冠肺炎疫情在全球范围的迅速蔓延,疫情防控一线以及广大普通民众对一次性医用手套的需求与日俱增,其标准也受到广泛关注。本文对中国国家标准涉及一次性使用医用手套的4项产品标准进行了解读,并分析它们与国外同种产品标准间的对应关系以及重点指标的差异。     

基于亲生物理论的医疗空间色彩设计研究

人类在漫长的进化过程中形成了对自然的本能需求,亲生物设计从生物进化的角度以更系统和更合理的方法将自然重新融入现代建筑环境,构建人与场所之间的积极交互。源于光、水、风、植物、动物等自然元素的亲生物色彩设计具有令人愉悦的艺术感染力,动态的视觉效果暗示着蓬勃的生命力,能够系统、清晰地传递复杂的环境信息,引发观者的情感共鸣和情感依恋。从环境疗愈的角度看,具有亲生物特征的色彩设计对改善患者在医疗空间中的体验,激发患者的自我康复能力具有重要意义。亲生物设计是可持续发展设计中不可或缺的一环,两者的结合有助于创造兼具低环境影响和高情感联系的设计,以实现真正和持久的可持续性发展。     

Visualization analysis of heart diseases using two‐dimensional electrocardiogram sequences

During the last years, there have been large advances in the understanding of cardiac mechanics, and in particular shape, motion and deformation. However, it is important to comprehend how these features interrelate and how they can be interpreted to refine the clinical diagnosis. The goal of our study is to illustrate the use of two‐dimensional ultrasound cardiac motion sequences on some typical cardiac pathologies. The method is applied to preprocessed data from two‐dimensional ultrasound sequences of four different subjects, namely, two healthy volunteers, one patient with left ventricular mechanical dyssynchrony from the left bundle branch block, and one patient with hypertrophic cardiomyopathy. The interpretation based on these features is corroborated with other analyzing techniques such as electrocardiogram and visual interpretation by experienced clinical observers. The proposed method realizes a computer program, which allows the user to easily obtain and visualize information about myocardial shape, motion (displacement and velocity), and deformation (strain) along the left ventricle myocardium wall during the cardiac cycle.     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

信息技术在医院病案统计中的应用

伴随着我国综合实力的不断提升,信息技术获得了突飞猛进的发展,如今,医疗改革如火如荼地进行,医院的信息管理工作同以往相比较也实现了极大的发展。病案统计工作是指相关工作人员对医院中患者的信息进行全面统计,若是这项工作完成得足够出色,会给医院后续的信息查询工作带来诸多方便和快捷,也能够在某种程度上使医院管理效率和质量实现一定幅度的提升。因此,将信息技术和医院的病案工管理工作进行密切结合,就成为病案管理人员应当重点思考的问题。病案统计工作是医院管理工作中的一个重点环节,病案统计结果能够为医院整体绩效带来十分深远的影响。因此,投入更多时间和精力来提高对这项工作的重视,显得十分关键和重要。文章对信息技术在医院病案统计中的应用展开了分析。