Ownership and use of tissue specimens for research

Rina Hakimian, JD, MPH; David Korn, MD

JAMA. 2004;292:2500-2505.

Academic and industrial scientists have sharply increased their demand for properly prepared and clinically annotated tissue samples that yield valuable insights into the origins and expressions of human disease. Historically, research on human tissue samples has been relatively unencumbered by federal regulations and occurred without delineation of ownership rights to the specimens, patient data, or research products. As regulations have become increasingly restrictive, and because clear ownership interests have never been established, the presumed right of researchers and institutions to collect, use, and dispose of specimens and their associated patient data has remained undefined and occasionally contentious. Recent examination of these issues by a US federal court resulted in a ruling that individuals do not retain rights of ownership or control of biological materials contributed for research, regardless of whether commercial benefit accrues. This article examines the legal, regulatory, and ethical framework within which human tissue research is currently conducted. We contend that because the benefits of medical knowledge derived from tissue research potentially accrue to all individuals and future generations (rather than a single recipient), society may justify an expansive use of these valuable resources for future studies.

     

ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval,high-performance computing,and grid technology

Objective and design

The design and implementation of ImageMiner, a software platform for performing comparative analysis of expression patterns in imaged microscopy specimens such as tissue microarrays (TMAs), is described. ImageMiner is a federated system of services that provides a reliable set of analytical and data management capabilities for investigative research applications in pathology. It provides a library of image processing methods, including automated registration, segmentation, feature extraction, and classification, all of which have been tailored, in these studies, to support TMA analysis. The system is designed to leverage high-performance computing machines so that investigators can rapidly analyze large ensembles of imaged TMA specimens. To support deployment in collaborative, multi-institutional projects, ImageMiner features grid-enabled, service-based components so that multiple instances of ImageMiner can be accessed remotely and federated.

Results

The experimental evaluation shows that: (1) ImageMiner is able to support reliable detection and feature extraction of tumor regions within imaged tissues; (2) images and analysis results managed in ImageMiner can be searched for and retrieved on the basis of image-based features, classification information, and any correlated clinical data, including any metadata that have been generated to describe the specified tissue and TMA; and (3) the system is able to reduce computation time of analyses by exploiting computing clusters, which facilitates analysis of larger sets of tissue samples.     

An architecture for research computing in health to support clinical and translational investigators with electronic patient data

ObjectiveObtaining electronic patient data, especially from electronic health record (EHR) systems, for clinical and translational research is difficult. Multiple research informatics systems exist but navigating the numerous applications can be challenging for scientists. This article describes Architecture for Research Computing in Health (ARCH), our institution’s approach for matching investigators with tools and services for obtaining electronic patient data.Materials and MethodsSupporting the spectrum of studies from populations to individuals, ARCH delivers a breadth of scientific functions—including but not limited to cohort discovery, electronic data capture, and multi-institutional data sharing—that manifest in specific systems—such as i2b2, REDCap, and PCORnet. Through a consultative process, ARCH staff align investigators with tools with respect to study design, data sources, and cost. Although most ARCH services are available free of charge, advanced engagements require fee for service.ResultsSince 2016 at Weill Cornell Medicine, ARCH has supported over 1200 unique investigators through more than 4177 consultations. Notably, ARCH infrastructure enabled critical coronavirus disease 2019 response activities for research and patient care.DiscussionARCH has provided a technical, regulatory, financial, and educational framework to support the biomedical research enterprise with electronic patient data. Collaboration among informaticians, biostatisticians, and clinicians has been critical to rapid generation and analysis of EHR data.ConclusionA suite of tools and services, ARCH helps match investigators with informatics systems to reduce time to science. ARCH has facilitated research at Weill Cornell Medicine and may provide a model for informatics and research leaders to support scientists elsewhere.     

Development of a HIPAA-compliant environment for translational research data and analytics

High-performance computing centers (HPC) traditionally have far less restrictive privacy management policies than those encountered in healthcare. We show how an HPC can be re-engineered to accommodate clinical data while retaining its utility in computationally intensive tasks such as data mining, machine learning, and statistics. We also discuss deploying protected virtual machines. A critical planning step was to engage the university''s information security operations and the information security and privacy office. Access to the environment requires a double authentication mechanism. The first level of authentication requires access to the university''s virtual private network and the second requires that the users be listed in the HPC network information service directory. The physical hardware resides in a data center with controlled room access. All employees of the HPC and its users take the university''s local Health Insurance Portability and Accountability Act training series. In the first 3 years, researcher count has increased from 6 to 58.     

A simulation based decision support system for a Health Promotion Center

This study represents the financial and qualitative evaluation of a Health Promotion Center of a private hospital located in a medium-sized town located in a predominantly agricultural area. The major objective of this study was to evaluate the impact of various pricing, advertising, and service strategies on profitability. The result is a pricing strategy which allows the center to reach their financial goal of breaking even while maintaining their service policies. The hospital, which serves a community of 500,000 people, recently developed a new concept called a “Health Promotion Center” (HPC). The HPC provides services such as fitness programs, nutrition awareness, rehabilitation and therapy, and child and adult care. For this purpose, a new building was constructed and the center became operational in June 1983. A variety of management options available to the center are described with evaluations of their usefulness. Evaluation methods include scenarios, stochastic simulations, and analyses of how the decision makers use these methods. The inclusion of risk management, model flexibility, and user involvement are stressed throughout the paper and are critical in the decision process.     

基于建构主义理论的病理学实验课教学实践与研究

目的探讨建构主义理论在病理学实验教学中的应用及意义。方法选取广东医学院2009级临床医学专业6个班的198名本科学生为研究对象;运用随机数字表将其分为实验组及对照组,每组99人。对照组主要采用常规的病理实验教学方式;实验组综合运用多种基于建构主义理论的教学方法。学期结束时分别对两组学生大体标本与切片标本观察能力、学习行为以及理论水平依照同一标准进行考核;并采用自制调查问卷进行无记名问卷调查。考核以及问卷调查结果运用SPSS13．0统计软件进行分析。两组考核结果的比较,采用独立样本t检验;问卷调查结果的比较,采用频数表资料两独立样本行Wilcoxon秩和检验,以P〈0．05为差异有统计学意义。结果实验组学生的大体标本与切片标本观察能力、理论水平以及学习行为得分均高于对照组（P〈0．01）;问卷调查结果显示,两组学生对问卷中所含问题的反馈,实验组学生满意度高于对照组,差异有统计学意义（P〈0．01）。结论综合运用多种基于建构主义理论的教学方法提高了学生的学习兴趣与积极性,明显改善了学生的学习行为与策略,显著提高了病理学实验课教学质量。     

GOPubMed:基于GO和MeSH的信息检索与分析研究

GOPubMed是一种基于PubMed的结果可视化和后处理类型的智能搜索引擎.从工作原理、关键技术以及扩展功能3个方面对其性能进行解析.研究显示,GOPubMed利用基于语义网的语义分类工具--GO(Gene Ontology,基因本体)和MeSH,对PubMed检索结果进行分类,帮助用户快速地找出最相关的命中文献,实现语义网与生物医学信息检索的完美结合,并能对检索结果从多角度进行可视化统计分析.     

Foundational biomedical informatics research in the clinical and translational science era: a call to action

Advances in clinical and translational science, along with related national-scale policy and funding mechanisms, have provided significant opportunities for the advancement of applied clinical research informatics (CRI) and translational bioinformatics (TBI). Such efforts are primarily oriented to application and infrastructure development and are critical to the conduct of clinical and translational research. However, they often come at the expense of the foundational CRI and TBI research needed to grow these important biomedical informatics subdisciplines and ensure future innovations. In light of this challenge, it is critical that a number of steps be taken, including the conduct of targeted advocacy campaigns, the development of community-accepted research agendas, and the continued creation of forums for collaboration and knowledge exchange. Such efforts are needed to ensure that the biomedical informatics community is able to advance CRI and TBI science in the context of the modern clinical and translational science era.Over the past decade the health and life sciences communities have experienced a marked and dramatic shift toward translational and team science-based approaches to both basic and applied research.^{1 2} This transition is due in part to policy and funding initiatives at the national level, such as the clinical and translational science award (CTSA) program. A common theme spanning this evolution is recognition of the critical need to apply biomedical informatics theories and methods to enable the collection, exchange, management, analysis and dissemination of multidimensional datasets and knowledge collections. For example, complex clinical phenotype data describing large populations must be integrated with similarly large amounts of genomic data in order to support the identification of clinically relevant phenotype–genotype correlations. These types of needs have catalyzed an explosion of informatics research and development targeting the clinical and translational research domains. Such efforts have enabled numerous advancements in applied clinical and translational research informatics knowledge and practice. However, at the same time, the maturation of clinical research informatics (CRI)¹ and translational bioinformatics (TBI)³ is at risk of failing to meet expectations if commensurate foundational research in those same areas is not conducted.     

基于分组的反转式教学在病理学授课中的应用与探讨

目的　对基于分组的反转式教学在病理学授课中的应用进行探讨。方法　随机选取临床医学本科四个班学生作为研究对象,将研究对象随机分为实验组和对照组,对照组（60人）采用反转式教学,实验组（60人）采用基于分组的反转式教学。采用学生实验考试和电子问卷调查评价教学效果,将数据整理后录入SPSS 20.0,组间比较采用t检验。结果　考试成绩比较和问卷调查结果表明,实验组的成绩[（28.1±0.7）分]明显高于对照组[（27.5±0.9）分],差异有统计学意义（P=0.008）。实验组学生的学习兴趣明显提高,自信心增强,同时学生的团队意识和独立思考能力、解决问题能力得到提升。结论　基于分组的反转式教学能有效提高教师的教学质量,增强学生的团队意识和合作精神,为学生以后的临床工作打下坚实基础。     

医学教学和科研标本的摄影方法探索

400016　重庆医科大学医学信息学院（佘颖）,生命科学研究院医学实验技术教研室（范京川）;400023 重庆市字水中学（谢续万、邹坤航、刘哲然、林欢、胡瑶、董江豪）     

SPINK1在前列腺癌中的基础及临床转化研究进展

在肿瘤发生和演进中发挥驱动作用的基因改变常可作为特异性诊断标志物、分子分型的依据和治疗新靶点。丝氨酸蛋白酶抑制因子Kazal1型(SPINK1)又被称为胰腺分泌性胰酶抑制因子(PSTI)或肿瘤相关性胰酶抑制因子(TATI),是一种由56个氨基酸残基组成的分泌性多肽,主要作用是抑制胰蛋白酶原等多种丝氨酸蛋白酶原活性。最近的研究提示,SPINK1可能通过发挥类生长因子作用,促进前列腺癌的生长和侵袭。SPINK1和前列腺癌患者的预后密切相关,可能是某些恶性程度高的前列腺癌的潜在治疗靶点。本课题组研究初步证实SPINK1的过表达与前列腺癌患者的临床不良预后呈正相关。本文通过对现阶段前列腺癌中SPINK1的基础及临床转化研究作简要综述,旨在强调其在前列腺癌的预后评价和治疗靶点选择上的重要意义。     

53例外伤性肝破裂的诊断及治疗体会

目的：探讨外伤性肝破裂的诊断及治疗方法。方法：回顾性分析我院2003年1月～2007年12月间收治的53例外伤性肝破裂患者的临床资料。其中经保守治疗15例,手术治疗38例。结果：治愈48例,5例死亡,死亡原因为失血性休克及多脏器功能衰竭。结论：外伤性肝破裂应及时诊断制订合理的治疗方案,准确评估患者的病情,选择正确的手术方式,积极处理合并伤,可降低病死率。     

基于虚拟现实技术的外科教学系统在外科手术器械教学中的应用

目的对基于虚拟现实技术的外科教学系统在外科手术器械教学中的应用效果进行评估。方法将81名临床医学专业五年级(2015级)医学生随机分为试验组(n=40)和对照组(n=41)。试验组采用基于虚拟现实技术的外科教学系统进行学习,对照组采用传统课堂教学方式进行学习。教学完成后两组接受相同形式的现场理论考核和教学满意度问卷调查。结果现场理论考核两组得分差异无统计学意义(P=0.179),但试验组在器械使用方法题的得分显著高于对照组,差异具有统计学意义(P=0.0047)。问卷调查显示,两组兴趣度差异无统计学意义(P=0.146),而试验组专注度高于对照组,差异具有统计学意义(P=0.026)。结论在外科手术器械教学中,基于虚拟现实技术的外科教学系统可以提高学生的学习兴趣和专注度,加深其对器械使用方法的理解,从而提高教学质量。     

The OneFlorida Data Trust: a centralized,translational research data infrastructure of statewide scope

The OneFlorida Data Trust is a centralized research patient data repository created and managed by the OneFlorida Clinical Research Consortium (“OneFlorida”). It comprises structured electronic health record (EHR), administrative claims, tumor registry, death, and other data on 17.2 million individuals who received healthcare in Florida between January 2012 and the present. Ten healthcare systems in Miami, Orlando, Tampa, Jacksonville, Tallahassee, Gainesville, and rural areas of Florida contribute EHR data, covering the major metropolitan regions in Florida. Deduplication of patients is accomplished via privacy-preserving entity resolution (precision 0.97–0.99, recall 0.75), thereby linking patients’ EHR, claims, and death data. Another unique feature is the establishment of mother-baby relationships via Florida vital statistics data. Research usage has been significant, including major studies launched in the National Patient-Centered Clinical Research Network (“PCORnet”), where OneFlorida is 1 of 9 clinical research networks. The Data Trust’s robust, centralized, statewide data are a valuable and relatively unique research resource.