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Gazula H, Tregidgo HFJ, Billot B, Balbastre Y, Williams-Ramirez J, Herisse R, Deden-Binder LJ, Casamitjana A, Melief EJ, Latimer CS, Kilgore MD, Montine M, Robinson E, Blackburn E, Marshall MS, Connors TR, Oakley DH, Frosch MP, Young SI, Van Leemput K, Dalca AV, Fischl B, MacDonald CL, Keene CD, Hyman BT, Iglesias JE. Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology. eLife 2024; 12:RP91398. [PMID: 38896568 PMCID: PMC11186625 DOI: 10.7554/elife.91398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024] Open
Abstract
We present open-source tools for three-dimensional (3D) analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (1) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (2) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer's Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer's disease cases and controls. The tools are available in our widespread neuroimaging suite 'FreeSurfer' (https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools).
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Affiliation(s)
- Harshvardhan Gazula
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Henry FJ Tregidgo
- Centre for Medical Image Computing, University College LondonLondonUnited Kingdom
| | - Benjamin Billot
- Computer Science and Artificial Intelligence Laboratory, MITCambridgeUnited States
| | - Yael Balbastre
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | | | - Rogeny Herisse
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Lucas J Deden-Binder
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Adria Casamitjana
- Centre for Medical Image Computing, University College LondonLondonUnited Kingdom
- Biomedical Imaging Group, Universitat Politècnica de CatalunyaBarcelonaSpain
| | - Erica J Melief
- BioRepository and Integrated Neuropathology (BRaIN) Laboratory and Precision Neuropathology Core, UW School of MedicineSeattleUnited States
| | - Caitlin S Latimer
- BioRepository and Integrated Neuropathology (BRaIN) Laboratory and Precision Neuropathology Core, UW School of MedicineSeattleUnited States
| | - Mitchell D Kilgore
- BioRepository and Integrated Neuropathology (BRaIN) Laboratory and Precision Neuropathology Core, UW School of MedicineSeattleUnited States
| | - Mark Montine
- BioRepository and Integrated Neuropathology (BRaIN) Laboratory and Precision Neuropathology Core, UW School of MedicineSeattleUnited States
| | - Eleanor Robinson
- Centre for Medical Image Computing, University College LondonLondonUnited Kingdom
| | - Emily Blackburn
- Centre for Medical Image Computing, University College LondonLondonUnited Kingdom
| | - Michael S Marshall
- Massachusetts Alzheimer Disease Research Center, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Theresa R Connors
- Massachusetts Alzheimer Disease Research Center, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Derek H Oakley
- Massachusetts Alzheimer Disease Research Center, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Matthew P Frosch
- Massachusetts Alzheimer Disease Research Center, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Sean I Young
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Koen Van Leemput
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
- Neuroscience and Biomedical Engineering, Aalto UniversityEspooFinland
| | - Adrian V Dalca
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
- Computer Science and Artificial Intelligence Laboratory, MITCambridgeUnited States
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | | | - C Dirk Keene
- BioRepository and Integrated Neuropathology (BRaIN) Laboratory and Precision Neuropathology Core, UW School of MedicineSeattleUnited States
| | - Bradley T Hyman
- Massachusetts Alzheimer Disease Research Center, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Juan E Iglesias
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
- Centre for Medical Image Computing, University College LondonLondonUnited Kingdom
- Computer Science and Artificial Intelligence Laboratory, MITCambridgeUnited States
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Gazula H, Tregidgo HFJ, Billot B, Balbastre Y, William-Ramirez J, Herisse R, Deden-Binder LJ, Casamitjana A, Melief EJ, Latimer CS, Kilgore MD, Montine M, Robinson E, Blackburn E, Marshall MS, Connors TR, Oakley DH, Frosch MP, Young SI, Van Leemput K, Dalca AV, FIschl B, Mac Donald CL, Keene CD, Hyman BT, Iglesias JE. Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.08.544050. [PMID: 37333251 PMCID: PMC10274889 DOI: 10.1101/2023.06.08.544050] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
We present open-source tools for 3D analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (i) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (ii) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer's Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer's disease cases and controls. The tools are available in our widespread neuroimaging suite "FreeSurfer" ( https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools ).
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Griffin CP, Paul CL, Alexander KL, Walker MM, Hondermarck H, Lynam J. Postmortem brain donations vs premortem surgical resections for glioblastoma research: viewing the matter as a whole. Neurooncol Adv 2022; 4:vdab168. [PMID: 35047819 PMCID: PMC8760897 DOI: 10.1093/noajnl/vdab168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
There have been limited improvements in diagnosis, treatment, and outcomes of primary brain cancers, including glioblastoma, over the past 10 years. This is largely attributable to persistent deficits in understanding brain tumor biology and pathogenesis due to a lack of high-quality biological research specimens. Traditional, premortem, surgical biopsy samples do not allow full characterization of the spatial and temporal heterogeneity of glioblastoma, nor capture end-stage disease to allow full evaluation of the evolutionary and mutational processes that lead to treatment resistance and recurrence. Furthermore, the necessity of ensuring sufficient viable tissue is available for histopathological diagnosis, while minimizing surgically induced functional deficit, leaves minimal tissue for research purposes and results in formalin fixation of most surgical specimens. Postmortem brain donation programs are rapidly gaining support due to their unique ability to address the limitations associated with surgical tissue sampling. Collecting, processing, and preserving tissue samples intended solely for research provides both a spatial and temporal view of tumor heterogeneity as well as the opportunity to fully characterize end-stage disease from histological and molecular standpoints. This review explores the limitations of traditional sample collection and the opportunities afforded by postmortem brain donations for future neurobiological cancer research.
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Affiliation(s)
- Cassandra P Griffin
- School of Medicine and Public Health, University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Cancer Biobank: NSW Regional Biospecimen and Research Services, University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Cancer Research Alliance, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Christine L Paul
- School of Medicine and Public Health, University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Cancer Research Alliance, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
- Priority Research Centre Cancer Research, Innovation and Translation, University of Newcastle, New South Wales, Australia
- Priority Research Centre Health Behaviour, University of Newcastle, New South Wales, Australia
| | - Kimberley L Alexander
- Neurosurgery Department, Chris O’Brien Lifehouse, Camperdown, New South Wales, Australia
- Brainstorm Brain Cancer Research, Brain and Mind Centre, The University of Sydney, New South Wales, Australia
- Neuropathology Department, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Marjorie M Walker
- School of Medicine and Public Health, University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Cancer Research Alliance, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Hubert Hondermarck
- Hunter Cancer Research Alliance, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute, Newcastle, New South Wales, Australia
- School of Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, New South Wales, Australia
| | - James Lynam
- School of Medicine and Public Health, University of Newcastle, Callaghan, New South Wales, Australia
- Hunter Cancer Research Alliance, University of Newcastle, Newcastle, New South Wales, Australia
- Department of Medical Oncology, Calvary Mater, Newcastle, New South Wales, Australia
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Overby CL, Maloney KA, Alestock TD, Chavez J, Berman D, Sharaf RM, Fitzgerald T, Kim EY, Palmer K, Shuldiner AR, Mitchell BD. Prioritizing Approaches to Engage Community Members and Build Trust in Biobanks: A Survey of Attitudes and Opinions of Adults within Outpatient Practices at the University of Maryland. J Pers Med 2015. [PMID: 26226006 PMCID: PMC4600147 DOI: 10.3390/jpm5030264] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background: Achieving high participation of communities representative of all sub-populations is needed in order to ensure broad applicability of biobank study findings. This study aimed to understand potentially mutable attitudes and opinions commonly correlated with biobank participation in order to inform approaches to promote participation in biobanks. Methods: Adults from two University of Maryland (UMD) Faculty Physicians, Inc. outpatient practices were invited to watch a video and complete a survey about a new biobank initiative. We used: Chi-square to assess the relationship between willingness to join the biobank and participant characteristics, other potentially mutable attitudes and opinions, and trust in the UMD. We also used t-test to assess the relationship with trust in medical research. We also prioritize proposed actions to improve attitudes and opinions about joining biobanks according to perceived responsiveness. Results: 169 participants completed the study, 51% of whom indicated a willingness to join the biobank. Willingness to join the biobank was not associated with age, gender, race, or education but was associated with respondent comfort sharing samples and clinical information, concerns related to confidentiality, potential for misuse of information, trust in UMD, and perceived health benefit. In ranked order, potential actions we surveyed that might alleviate some of these concerns include: increase chances to learn more about the biobank, increase opportunities to be updated, striving to put community concerns first, including involving community members as leaders of biobank research, and involving community members in decision making. Conclusions: This study identified several attitudes and opinions that influence decisions to join a biobank, including many concerns that could potentially be addressed by engaging community members. We also demonstrate our method of prioritizing ways to improve attitudes and opinions about joining a biobank according to perceived responsiveness.
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Affiliation(s)
- Casey Lynnette Overby
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
- Center for Health-related Informatics and Bioimaging, University of Maryland, Baltimore, MD 21201, USA.
| | - Kristin A Maloney
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
| | - Tameka DeShawn Alestock
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
| | - Justin Chavez
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
- University of Maryland, Baltimore County, Baltimore, MD 21250, USA.
| | - David Berman
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
- King's College London, London WC2R 2LS, UK.
| | - Reem Maged Sharaf
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
| | - Tom Fitzgerald
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
| | - Eun-Young Kim
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
- Department of Clinical Pharmacology, Inje University Busan Paik Hospital, Busan 614-735, Korea.
| | - Kathleen Palmer
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
| | - Alan R Shuldiner
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
| | - Braxton D Mitchell
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
- Geriatric Research and Education Clinical Center, Veterans Affairs Maryland Health Care System, Baltimore, MD 21201, USA.
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Ravid R. The uniqueness of biobanks for neurological and psychiatric diseases: potentials and pitfalls. Pathobiology 2015; 81:237-244. [PMID: 25792212 DOI: 10.1159/000369886] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVES Central nervous system (CNS) biobanks are facing difficult and specific challenges due to the sensitive issue of collecting specimens of the CNS, and especially the brain. At present, there is no global network/central database to serve researchers, clinicians and pharma companies, or to supply the special specimens and the accompanying data in sufficient numbers and detail, respectively. The main challenge/objective is to standardize and harmonize all the facets involved in CNS biobanking in order to maximize efficient sample collection. METHODS Since the number of CNS biospecimens stored in existing biobanks is relatively limited and the accompanying data are not always readily available and hard to identify, we propose using optimal procedures for handling and storage of these specimens, and the global standardization of the cliniconeuropathological diagnostic criteria. RESULTS One of the prominent achievements of the current global activity in brain tissue biobanks (BTB-banks) is the development of an inventory of international standards, available specimens and concomitant data, and national registries. CONCLUSIONS Taking into consideration the huge variety of the specimens stored in different repositories and the enormous differences in medicolegal systems and ethics regulations in different countries, we strongly recommend that healthcare systems and institutions who host BTB-banks make efforts to secure adequate funding for the infrastructure and daily activities. BTB-banks will refine standard operating procedures and their internal guides of best practices/codes of conduct. This in turn will enable the BTB-banks to share the collected specimens and data with the largest possible number of researchers, aiming at maximal scientific spin-off and advance of public health research.
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Affiliation(s)
- Rivka Ravid
- BrainBank Consultants, Amsterdam, The Netherlands
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Changes in rat urinary porphyrin profiles predict the magnitude of the neurotoxic effects induced by a mixture of lead, arsenic and manganese. Neurotoxicology 2014; 45:168-77. [DOI: 10.1016/j.neuro.2014.10.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Revised: 10/21/2014] [Accepted: 10/21/2014] [Indexed: 12/19/2022]
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Meir K, Gaffney EF, Simeon-Dubach D, Ravid R, Watson PH, Schacter B, Morente And The Marble Arch International Working Group On Biobanking MM. The human face of biobank networks for translational research. Biopreserv Biobank 2014; 9:279-85. [PMID: 24850340 DOI: 10.1089/bio.2011.0018] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The biobanking literature frequently addresses donor and societal issues surrounding biobanking, but the biobanker's perspective is rarely highlighted. While not comprehensive, this article offers an overview of the human aspects of biobanking from the viewpoint of biobank personnel-from biobank formation, through the process, and in addressing post-biobanking issues. As every biobank and biobank network may differ, such factors may vary. Before biobanking can commence, the purpose of the biobank network must be defined, and buy-in achieved from many stakeholders. An attitude of trust and sharing is essential, as is good communication. Developing a biobank is time consuming and laborious. Forming a network requires significantly more time due to the need for cross-institutional harmonization of policies, procedures, information technology considerations, and ethics. Circumstances may dictate whether development occurs top-down and/or bottom-up, as well as whether network management may be independent or by personnel from participating biobanks. Funding tends to be a prominent issue for biobanks and networks alike. In particular, networks function optimally with some level of government support, particularly for personnel. Quality biospecimen collection involves meticulously documented coordination with a network of medical and nursing staff. Examining and sampling operative specimens requires timely collaboration between the surgical and pathology teams. "Catch rates" for samples may be difficult to predict and may occur at a frequency less than anticipated due to factors related to the institution, staff, or specimen. These factors may affect specimen quality, and have a downstream effect on competition for specimens for research. Thus, release of samples requires a fair, carefully constructed sample access policy, usually incorporating an incentive for researchers, and an encouragement to form collaborations. Finally, the public and patient groups should aim to understand the benefits of a biobank network, so that patient care is improved through coordinated biobanking activity.
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Affiliation(s)
- Karen Meir
- 1 Department of Pathology, Hadassah-Hebrew University Medical Center , Jerusalem, Israel
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2012 best practices for repositories collection, storage, retrieval, and distribution of biological materials for research international society for biological and environmental repositories. Biopreserv Biobank 2014; 10:79-161. [PMID: 24844904 DOI: 10.1089/bio.2012.1022] [Citation(s) in RCA: 133] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Third Edition [Formula: see text] [Box: see text] Printed with permission from the International Society for Biological and Environmental Repositories (ISBER) © 2011 ISBER All Rights Reserved Editor-in-Chief Lori D. Campbell, PhD Associate Editors Fay Betsou, PhD Debra Leiolani Garcia, MPA Judith G. Giri, PhD Karen E. Pitt, PhD Rebecca S. Pugh, MS Katherine C. Sexton, MBA Amy P.N. Skubitz, PhD Stella B. Somiari, PhD Individual Contributors to the Third Edition Jonas Astrin, Susan Baker, Thomas J. Barr, Erica Benson, Mark Cada, Lori Campbell, Antonio Hugo Jose Froes Marques Campos, David Carpentieri, Omoshile Clement, Domenico Coppola, Yvonne De Souza, Paul Fearn, Kelly Feil, Debra Garcia, Judith Giri, William E. Grizzle, Kathleen Groover, Keith Harding, Edward Kaercher, Joseph Kessler, Sarah Loud, Hannah Maynor, Kevin McCluskey, Kevin Meagher, Cheryl Michels, Lisa Miranda, Judy Muller-Cohn, Rolf Muller, James O'Sullivan, Karen Pitt, Rebecca Pugh, Rivka Ravid, Katherine Sexton, Ricardo Luis A. Silva, Frank Simione, Amy Skubitz, Stella Somiari, Frans van der Horst, Gavin Welch, Andy Zaayenga 2012 Best Practices for Repositories: Collection, Storage, Retrieval and Distribution of Biological Materials for Research INTERNATIONAL SOCIETY FOR BIOLOGICAL AND ENVIRONMENTAL REPOSITORIES (ISBER) INTRODUCTION T he availability of high quality biological and environmental specimens for research purposes requires the development of standardized methods for collection, long-term storage, retrieval and distribution of specimens that will enable their future use. Sharing successful strategies for accomplishing this goal is one of the driving forces for the International Society for Biological and Environmental Repositories (ISBER). For more information about ISBER see www.isber.org . ISBER's Best Practices for Repositories (Best Practices) reflect the collective experience of its members and has received broad input from other repository professionals. Throughout this document effective practices are presented for the management of specimen collections and repositories. The term "Best Practice" is used in cases where a level of operation is indicated that is above the basic recommended practice or more specifically designates the most effective practice. It is understood that repositories in certain locations or with particular financial constraints may not be able to adhere to each of the items designated as "Best Practices". Repositories fitting into either of these categories will need to decide how they might best adhere to these recommendations within their particular circumstances. While adherence to ISBER Best Practices is strictly on a voluntary basis, it is important to note that some aspects of specimen management are governed by national/federal, regional and local regulations. The reader should refer directly to regulations for their national/federal, regional and local requirements, as appropriate. ISBER has strived to include terminology appropriate to the various specimen types covered under these practices, but here too, the reader should take steps to ensure the appropriateness of the recommendations to their particular repository type prior to the implementation of any new approaches. Important terms within the document are italicized when first used in a section and defined in the glossary. The ISBER Best Practices are periodically reviewed and revised to reflect advances in research and technology. The third edition of the Best Practices builds on the foundation established in the first and second editions which were published in 2005 and 2008, respectively.
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Tsai A, Malek-Ahmadi M, Kahlon V, Sabbagh MN. Differences in Cerebrospinal Fluid Biomarkers between Clinically Diagnosed Idiopathic Normal Pressure Hydrocephalus and Alzheimer's Disease. ACTA ACUST UNITED AC 2014; 4. [PMID: 25937995 PMCID: PMC4415860 DOI: 10.4172/2161-0460.1000150] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Objective In the present study, cerebrospinal fluid (CSF) profiles were assessed to determine how idiopathic normal pressure hydrocephalus (NPH) and Alzheimer’s disease (AD) differs. Methods Subjects were drawn from patients who underwent lumbar punctures as part of their diagnostic evaluations in the Banner Sun Health Research Institute Memory Disorders clinic. The clinical sample included 11 iNPH subjects (mean age 81.36±2.58) and 11 AD subjects (mean age 61.46±8.24). Concentrations of amyloid-β (Aβ42), total-tau (t-tau), phospho-tau181 (p-tau) Aβ42, and an Aβ42-Tau Index (ATI) were measured by commercial assay (Athena Diagnostics). and compared to each other. The Mann-Whitney test was used to assess group differences on the raw values for Aβ42, t-tau, p-tau, ATI, age, education, and MMSE. Results In a univariate analysis, p-tau was found to be significantly (P = 0.009) lower in patients diagnosed with iNPH than those with AD. Amyloid-β (Aβ42), total-tau (t-tau) did not differ between groups. In multi-variate analysis, the differences in p-tau between groups did not differ. Conclusion Although age could represent a significant confound, p-tau is significantly lower in iNPH compared to AD. P-tau would be expected to increase with age but in this sample is lower suggesting the difference might be explained by the underlying condition.
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Affiliation(s)
- Andrew Tsai
- The Cleo Roberts Center for Clinical Research, Banner-Sun Health Research Institute, AZ, USA
| | - Michael Malek-Ahmadi
- The Cleo Roberts Center for Clinical Research, Banner-Sun Health Research Institute, AZ, USA
| | - Vickram Kahlon
- The Cleo Roberts Center for Clinical Research, Banner-Sun Health Research Institute, AZ, USA
| | - Marwan N Sabbagh
- The Cleo Roberts Center for Clinical Research, Banner-Sun Health Research Institute, AZ, USA
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Ravid R, Fasano M. The importance of harmonizing and standardizing CNS biomarkers of neurodegeneration. FUTURE NEUROLOGY 2012. [DOI: 10.2217/fnl.12.69] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Rivka Ravid
- Brain bank Cionsultants, Royal Dutch Academy of Sciences, Meibergdreef 47, 1105 BA Amsterdam, The Netherlands
| | - Mauro Fasano
- University of Insubria, via Alberto da, Giussano 12, I-21052 Busto Arsizio, Italy
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Ravid R, Ferrer I. Brain banks as key part of biochemical and molecular studies on cerebral cortex involvement in Parkinson’s disease. FEBS J 2012; 279:1167-76. [DOI: 10.1111/j.1742-4658.2012.08518.x] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Späth MB, Grimson J. Applying the archetype approach to the database of a biobank information management system. Int J Med Inform 2010; 80:205-26. [PMID: 21131230 DOI: 10.1016/j.ijmedinf.2010.11.002] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2010] [Revised: 11/01/2010] [Accepted: 11/02/2010] [Indexed: 11/17/2022]
Abstract
PURPOSE The purpose of this study is to investigate the feasibility of applying the openEHR archetype approach to modelling the data in the database of an existing proprietary biobank information management system. A biobank information management system stores the clinical/phenotypic data of the sample donor and sample related information. The clinical/phenotypic data is potentially sourced from the donor's electronic health record (EHR). The study evaluates the reuse of openEHR archetypes that have been developed for the creation of an interoperable EHR in the context of biobanking, and proposes a new set of archetypes specifically for biobanks. The ultimate goal of the research is the development of an interoperable electronic biomedical research record (eBMRR) to support biomedical knowledge discovery. METHODS The database of the prostate cancer biobank of the Irish Prostate Cancer Research Consortium (PCRC), which supports the identification of novel biomarkers for prostate cancer, was taken as the basis for the modelling effort. First the database schema of the biobank was analyzed and reorganized into archetype-friendly concepts. Then, archetype repositories were searched for matching archetypes. Some existing archetypes were reused without change, some were modified or specialized, and new archetypes were developed where needed. The fields of the biobank database schema were then mapped to the elements in the archetypes. Finally, the archetypes were arranged into templates specifically to meet the requirements of the PCRC biobank. RESULTS A set of 47 archetypes was found to cover all the concepts used in the biobank. Of these, 29 (62%) were reused without change, 6 were modified and/or extended, 1 was specialized, and 11 were newly defined. These archetypes were arranged into 8 templates specifically required for this biobank. A number of issues were encountered in this research. Some arose from the immaturity of the archetype approach, such as immature modelling support tools, difficulties in defining high-quality archetypes and the problem of overlapping archetypes. In addition, the identification of suitable existing archetypes was time-consuming and many semantic conflicts were encountered during the process of mapping the PCRC BIMS database to existing archetypes. These include differences in the granularity of documentation, in metadata-level versus data-level modelling, in terminologies and vocabularies used, and in the amount of structure imposed on the information to be recorded. Furthermore, the current way of modelling the sample entity was found to be cumbersome in the sample-centric activity of biobanking. CONCLUSIONS The archetype approach is a promising approach to create a shareable eBMRR based on the study participant/donor for biobanks. Many archetypes originally developed for the EHR domain can be reused to model the clinical/phenotypic and sample information in the biobank context, which validates the genericity of these archetypes and their potential for reuse in the context of biomedical research. However, finding suitable archetypes in the repositories and establishing an exact mapping between the fields in the PCRC BIMS database and the elements of existing archetypes that have been designed for clinical practice can be challenging and time-consuming and involves resolving many common system integration conflicts. These may be attributable to differences in the requirements for information documentation between clinical practice and biobanking. This research also recognized the need for better support tools, modelling guidelines and best practice rules and reconfirmed the need for better domain knowledge governance. Furthermore, the authors propose that the establishment of an independent sample record with the sample as record subject should be investigated. The research presented in this paper is limited by the fact that the new archetypes developed during this research are based on a single biobank instance. These new archetypes may not be complete, representing only those subsets of items required by this particular database. Nevertheless, this exercise exposes some of the gaps that exist in the archetype modelling landscape and highlights the concepts that need to be modelled with archetypes to enable the development of an eBMRR.
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Affiliation(s)
- Melanie Bettina Späth
- Centre for Health Informatics, School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, Ireland.
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Legge SD, Hachinski V. Vascular cognitive impairment (VCI): Progress towards knowledge and treatment. Dement Neuropsychol 2010; 4:4-13. [PMID: 29213654 PMCID: PMC5619524 DOI: 10.1590/s1980-57642010dn40100002] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2009] [Accepted: 12/17/2009] [Indexed: 01/08/2023] Open
Abstract
Until recently, the study of cognitive impairment as a manifestation of cerebrovascular disease (CVD) has been hampered by the lack of common standards for assessment. The term vascular cognitive impairment (VCI) encompasses all levels of cognitive decline associated with CVD from mild deficits in one or more cognitive domains to crude dementia syndrome. VCI incorporates the complex interactions among classic vascular risk factors (i.e. arterial hypertension, high cholesterol, and diabetes), CVD subtypes, and Alzheimer's Disease (AD) pathology. VCI may be the earliest, commonest, and subtlest manifestation of CVD and can be regarded as a highly prevalent and preventable syndrome. However, cognition is not a standardized outcome measure in clinical trials assessing functional ability after stroke. Furthermore, with the exception of anti-hypertensive medications, the impact of either preventive or acute stroke treatments on cognitive outcome is not known. Although clinical, epidemiological, neuroimaging, and experimental data support the VCI concept, there is a lack of integrated knowledge on the role played by the most relevant pathophysiological mechanisms involved in several neurological conditions including stroke and cognitive impairment such as excitotoxicity, apoptosis, mitochondrial DNA damage, oxidative stress, disturbed neurotransmitter release, and inflammation. For this reason, in 2006 the National Institute of Neurological Disorders and Stroke (NINDS) and the Canadian Stroke Network (CSN) defined a set of data elements to be collected in future studies aimed at defining VCI etiology, clinical manifestations, predictive factors, and treatment. These recommendations represent the first step toward developing diagnostic criteria for VCI based on sound knowledge rather than on hypotheses. The second step will be to integrate all studies using the agreed methodologies. This is likely to accelerate the search for answers.
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Affiliation(s)
- Silvia Di Legge
- MD, PhD, Stroke Unit, Department of Neuroscience,
University Tor Vergata, Rome, Italy
| | - Vladimir Hachinski
- MD, FRCPC, DSc, Department of Clinical Neurological
Sciences, London Health Sciences Centre (LHSC) and University of Western Ontario
(UWO), London, ON, Canada
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