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Irvin R, Landry G, Jones MR, James A, Schexnayder J, Rodriguez S, Wendell D, Barthe K, Britton E, LeSar K, Manogue S, Sugarman OK, Brown C, Burgess S, Mehta SH, Thomas DL, Robinson W. High prevalence of hepatitis C virus infection among incarcerated persons: Results from the Louisiana Hepatitis C Elimination Plan's opt-out testing program in prisons. J Viral Hepat 2024. [PMID: 38758571 DOI: 10.1111/jvh.13941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/01/2024] [Accepted: 03/31/2024] [Indexed: 05/18/2024]
Abstract
In the United States, modelling studies suggest a high prevalence of hepatitis C virus (HCV) infection in incarcerated populations. However, limited HCV testing has been conducted in prisons. Through the Louisiana Hepatitis C Elimination Plan, persons incarcerated in the eight state prisons were offered HCV testing from 20 September 2019 to 14 July 2022, and facility entry/exit HCV testing was introduced. Multivariable logistic regression was used to evaluate associations with HCV antibody (anti-HCV) positivity and viremia. Of 17,231 persons in the eight state prisons screened for anti-HCV, 95.1% were male, 66.7% were 30-57 years old, 3% were living with HIV, 68.2% were Black and 2904 (16.9%) were anti-HCV positive. HCV RNA was detected in 69.3% of anti-HCV positive individuals tested. In the multivariable model, anti-HCV positivity was associated with older age including those 30-57 (odds ratio [OR] 3.53, 95% confidence interval [CI] 2.96-4.20) and those ≥58 (OR 10.43, 95% CI 8.66-12.55) as compared to those ≤29 years of age, living with HIV (OR 1.68, 95% CI 1.36-2.07), hepatitis B (OR 1.83, 95% CI 1.25-2.69) and syphilis (OR 1.51, 95% CI 1.23-1.86). HCV viremia was associated with male sex (OR 1.89, 95% CI 1.36-2.63) and Black race (OR 1.42, 95% CI 1.20-1.68). HCV prevalence was high in the state prisons in Louisiana compared to community estimates. To the extent that Louisiana is representative, to eliminate HCV in the United States, it will be important for incarcerated persons to have access to HCV testing and treatment.
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Affiliation(s)
- Risha Irvin
- Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Gia Landry
- Ochsner Health System, New Orleans, Louisiana, USA
- Louisiana Department of Health, Office of Public Health, New Orleans, Louisiana, USA
| | - Miranda R Jones
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Anthony James
- Louisiana Department of Health, Office of Public Health, New Orleans, Louisiana, USA
| | - Jean Schexnayder
- Louisiana Department of Health, Office of Public Health, New Orleans, Louisiana, USA
| | - Stacye Rodriguez
- Louisiana Department of Public Safety and Corrections, Baton Rouge, Louisiana, USA
| | - Deborah Wendell
- Louisiana Department of Health, Office of Public Health, New Orleans, Louisiana, USA
- Louisiana State University Health Sciences Center School of Public Health, New Orleans, Louisiana, USA
| | - Kathryn Barthe
- Louisiana Department of Health, Office of Public Health, New Orleans, Louisiana, USA
| | - Elizabeth Britton
- Louisiana Department of Health, Office of Public Health, New Orleans, Louisiana, USA
| | - Kendra LeSar
- Louisiana Department of Health, Office of Public Health, New Orleans, Louisiana, USA
| | - Sean Manogue
- Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Olivia K Sugarman
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Cynthia Brown
- Louisiana Department of Health, Office of Public Health, New Orleans, Louisiana, USA
| | - Samuel Burgess
- Louisiana Department of Health, Office of Public Health, New Orleans, Louisiana, USA
| | - Shruti H Mehta
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - David L Thomas
- Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - William Robinson
- Louisiana Department of Health, Office of Public Health, New Orleans, Louisiana, USA
- Louisiana State University Health Sciences Center School of Public Health, New Orleans, Louisiana, USA
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2
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Kandathil AJ, Thomas DL. The Blood Virome: A new frontier in biomedical science. Biomed Pharmacother 2024; 175:116608. [PMID: 38703502 DOI: 10.1016/j.biopha.2024.116608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/12/2024] [Accepted: 04/17/2024] [Indexed: 05/06/2024] Open
Abstract
Recent advances in metagenomic testing opened a new window into the mammalian blood virome. Comprised of well-known viruses like human immunodeficiency virus, hepatitis C virus, and hepatitis B virus, the virome also includes many other eukaryotic viruses and phages whose medical significance, lifecycle, epidemiology, and impact on human health are less well known and thus regarded as commensals. This review synthesizes available information for the so-called commensal virome members that circulate in the blood of humans considering their restriction to and interaction with the human host, their natural history, and their impact on human health and physiology.
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Affiliation(s)
- Abraham J Kandathil
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David L Thomas
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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3
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Suzuki Y, Clement P, Dai W, Dolui S, Fernández-Seara M, Lindner T, Mutsaerts HJMM, Petr J, Shao X, Taso M, Thomas DL. ASL lexicon and reporting recommendations: A consensus report from the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91:1743-1760. [PMID: 37876299 PMCID: PMC10950547 DOI: 10.1002/mrm.29815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 06/22/2023] [Accepted: 07/13/2023] [Indexed: 10/26/2023]
Abstract
The 2015 consensus statement published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group and the European Cooperation in Science and Technology ( COST) Action ASL in Dementia aimed to encourage the implementation of robust arterial spin labeling (ASL) perfusion MRI for clinical applications and promote consistency across scanner types, sites, and studies. Subsequently, the recommended 3D pseudo-continuous ASL sequence has been implemented by most major MRI manufacturers. However, ASL remains a rapidly and widely developing field, leading inevitably to further divergence of the technique and its associated terminology, which could cause confusion and hamper research reproducibility. On behalf of the ISMRM Perfusion Study Group, and as part of the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI), the ASL Lexicon Task Force has been working on the development of an ASL Lexicon and Reporting Recommendations for perfusion imaging and analysis, aiming to (1) develop standardized, consensus nomenclature and terminology for the broad range of ASL imaging techniques and parameters, as well as for the physiological constants required for quantitative analysis; and (2) provide a community-endorsed recommendation of the imaging parameters that we encourage authors to include when describing ASL methods in scientific reports/papers. In this paper, the sequences and parameters in (pseudo-)continuous ASL, pulsed ASL, velocity-selective ASL, and multi-timepoint ASL for brain perfusion imaging are included. However, the content of the lexicon is not intended to be limited to these techniques, and this paper provides the foundation for a growing online inventory that will be extended by the community as further methods and improvements are developed and established.
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Affiliation(s)
- Yuriko Suzuki
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Patricia Clement
- Department of Medical Imaging, Ghent University Hospital, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Weiying Dai
- State University of New York at Binghamton, Binghamton, NY, USA
| | - Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Maria Fernández-Seara
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | | | - Henk JMM Mutsaerts
- Department of Radiology and Nuclear medicine, Amsterdam Neuroscience, Amsterdam University Medical Center, the Netherlands, Amsterdam
| | - Jan Petr
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | - Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Manuel Taso
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
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Gupta N, Swindells S, Scarsi KK, Furl R, Thomas DL, Weld ED, Ofimboudem JD, Desalegn H, Hamid S, Rosas ADLT, Miranda AE, Owen A, Rannard S, Hiebert L, Sun K, Ward JW. Preferences and feasibility of long-acting technologies for treatment of hepatitis C virus in low- and middle-income countries: A survey of providers and policymakers. J Viral Hepat 2024; 31:221-232. [PMID: 38545826 DOI: 10.1111/jvh.13921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/15/2023] [Accepted: 01/10/2024] [Indexed: 04/18/2024]
Abstract
Long-acting technologies (LATs) for hepatitis C virus (HCV) are under development as a strategy to improve linkage to care, treatment adherence and outcomes. We conducted a survey of HCV treatment prescribers and HCV policymakers in low- and middle-income countries (LMICs) regarding acceptability and feasibility of HCV LATs. We included one-time intramuscular injection, subdermal implant and transdermal patch as potential LAT options. We surveyed participants regarding optimal health system and patient characteristics, concerns, potential barriers, overall feasibility and preferences for HCV LAT as compared to daily oral medication. Overall, 122 providers and 50 policymakers from 42 LMICs completed the survey. Among providers, 93% (113/122) expressed willingness to prescribe LAT and 72% (88/120) of providers preferred LAT if provided at comparable efficacy, safety and cost as current oral treatments. Of providers preferring HCV LAT to daily oral medication, 67% (59/88) preferred injection, 24% (21/88) preferred patch and 9% (8/88) preferred implant. Only 20% (24/122) would prescribe LAT if it were more costly than oral treatment. In regression analysis, no provider characteristics were associated with preference for LAT over oral treatment. Policymakers reported high likelihood that LAT would be included in treatment guidelines (42/50; 84%) and national drug formularies (39/50; 78%) if efficacy, safety and cost were similar to oral treatment. HCV LATs could advance progress to HCV elimination in LMICs by diversifying treatment options to improve treatment coverage and outcomes. Provider preferences from LMICs are a critical consideration in the development of HCV LATs to ensure its early and equitable availability in LMICs.
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Affiliation(s)
- Neil Gupta
- Coalition for Global Hepatitis Elimination, The Task Force for Global Health, Decatur, Georgia, USA
| | - Susan Swindells
- Section of Infectious Diseases, Department of Internal Medicine, The University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Kimberly K Scarsi
- Department of Pharmacy Practice and Science, College of Pharmacy, The University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Renae Furl
- Section of Infectious Diseases, Department of Internal Medicine, The University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - David L Thomas
- Division of Infectious Diseases, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ethel D Weld
- Division of Infectious Diseases, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Division of Clinical Pharmacology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Hailemichael Desalegn
- Medical Department, St. Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia
| | - Saeed Hamid
- Section of Gastroenterology, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | | | - Angelica E Miranda
- Post-Graduation Program in Infectious Diseases, Federal University of Espirito Santo, Vitória, Brazil
| | - Andrew Owen
- Department of Pharmacology and Therapeutics, Centre of Excellence in Long acting Therapeutics (CELT), University of Liverpool, Liverpool, UK
| | - Steve Rannard
- Department of Chemistry, Centre of Excellence in Long acting Therapeutics (CELT), University of Liverpool, Liverpool, UK
| | - Lindsey Hiebert
- Coalition for Global Hepatitis Elimination, The Task Force for Global Health, Decatur, Georgia, USA
| | - Katherine Sun
- Coalition for Global Hepatitis Elimination, The Task Force for Global Health, Decatur, Georgia, USA
| | - John W Ward
- Coalition for Global Hepatitis Elimination, The Task Force for Global Health, Decatur, Georgia, USA
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Fan H, Mutsaerts HJ, Anazodo U, Arteaga D, Baas KP, Buchanan C, Camargo A, Keil VC, Lin Z, Lindner T, Hirschler L, Hu J, Padrela BE, Taghvaei M, Thomas DL, Dolui S, Petr J. ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): ASL pipeline inventory. Magn Reson Med 2024; 91:1787-1802. [PMID: 37811778 PMCID: PMC10950546 DOI: 10.1002/mrm.29869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 08/21/2023] [Accepted: 08/30/2023] [Indexed: 10/10/2023]
Abstract
PURPOSE To create an inventory of image processing pipelines of arterial spin labeling (ASL) and list their main features, and to evaluate the capability, flexibility, and ease of use of publicly available pipelines to guide novice ASL users in selecting their optimal pipeline. METHODS Developers self-assessed their pipelines using a questionnaire developed by the Task Force 1.1 of the ISMRM Open Science Initiative for Perfusion Imaging. Additionally, each publicly available pipeline was evaluated by two independent testers with basic ASL experience using a scoring system created for this purpose. RESULTS The developers of 21 pipelines filled the questionnaire. Most pipelines are free for noncommercial use (n = 18) and work with the standard NIfTI (Neuroimaging Informatics Technology Initiative) data format (n = 15). All pipelines can process standard 3D single postlabeling delay pseudo-continuous ASL images and primarily differ in their support of advanced sequences and features. The publicly available pipelines (n = 9) were included in the independent testing, all of them being free for noncommercial use. The pipelines, in general, provided a trade-off between ease of use and flexibility for configuring advanced processing options. CONCLUSION Although most ASL pipelines can process the common ASL data types, only some (namely, ASLPrep, ASLtbx, BASIL/Quantiphyse, ExploreASL, and MRICloud) are well-documented, publicly available, support multiple ASL types, have a user-friendly interface, and can provide a useful starting point for ASL processing. The choice of an optimal pipeline should be driven by specific data to be processed and user experience, and can be guided by the information provided in this ASL inventory.
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Affiliation(s)
- Hongli Fan
- The Johns Hopkins School of Medicine, Department of Biomedical Engineering, Baltimore, Maryland, USA
- MR Research and Development, Siemens Medical Solutions USA, Inc., Dallas, Texas, USA
| | - Henk J.M.M. Mutsaerts
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Udunna Anazodo
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Daniel Arteaga
- Ascension Saint Thomas Hospital, Nashville, Tennessee, USA
| | - Koen P.A. Baas
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Amsterdam UMC, Location AMC, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands
| | - Charlotte Buchanan
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, United Kingdom
| | - Aldo Camargo
- School of Medicine, Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland of Baltimore
| | - Vera C. Keil
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Zixuan Lin
- The Johns Hopkins School of Medicine, Department of Biomedical Engineering, Baltimore, Maryland, USA
| | - Thomas Lindner
- Department of Diagnostic and Interventional Neuroradiology, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Lydiane Hirschler
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, the Netherlands
| | - Jian Hu
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, United Kingdom
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, United Kingdom
| | - Beatriz E. Padrela
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Mohammad Taghvaei
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - David L. Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Jan Petr
- Amsterdam UMC, location Vrije Universiteit Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
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Duchen D, Clipman SJ, Vergara C, Thio CL, Thomas DL, Duggal P, Wojcik GL. A hepatitis B virus (HBV) sequence variation graph improves alignment and sample-specific consensus sequence construction. PLoS One 2024; 19:e0301069. [PMID: 38669259 PMCID: PMC11051683 DOI: 10.1371/journal.pone.0301069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/09/2024] [Indexed: 04/28/2024] Open
Abstract
Nearly 300 million individuals live with chronic hepatitis B virus (HBV) infection (CHB), for which no curative therapy is available. As viral diversity is associated with pathogenesis and immunological control of infection, improved methods to characterize this diversity could aid drug development efforts. Conventionally, viral sequencing data are mapped/aligned to a reference genome, and only the aligned sequences are retained for analysis. Thus, reference selection is critical, yet selecting the most representative reference a priori remains difficult. We investigate an alternative pangenome approach which can combine multiple reference sequences into a graph which can be used during alignment. Using simulated short-read sequencing data generated from publicly available HBV genomes and real sequencing data from an individual living with CHB, we demonstrate alignment to a phylogenetically representative 'genome graph' can improve alignment, avoid issues of reference ambiguity, and facilitate the construction of sample-specific consensus sequences more genetically similar to the individual's infection. Graph-based methods can, therefore, improve efforts to characterize the genetics of viral pathogens, including HBV, and have broader implications in host-pathogen research.
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Affiliation(s)
- Dylan Duchen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Center for Biomedical Data Science, Yale School of Medicine, New Haven, CT, United States of America
| | - Steven J. Clipman
- Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Candelaria Vergara
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Chloe L. Thio
- Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - David L. Thomas
- Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Priya Duggal
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Genevieve L. Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
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7
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Woods JG, Achten E, Asllani I, Bolar DS, Dai W, Detre JA, Fan AP, Fernández-Seara MA, Golay X, Günther M, Guo J, Hernandez-Garcia L, Ho ML, Juttukonda MR, Lu H, MacIntosh BJ, Madhuranthakam AJ, Mutsaerts HJ, Okell TW, Parkes LM, Pinter N, Pinto J, Qin Q, Smits M, Suzuki Y, Thomas DL, Van Osch MJP, Wang DJJ, Warnert EAH, Zaharchuk G, Zelaya F, Zhao M, Chappell MA. Recommendations for quantitative cerebral perfusion MRI using multi-timepoint arterial spin labeling: Acquisition, quantification, and clinical applications. Magn Reson Med 2024. [PMID: 38594906 DOI: 10.1002/mrm.30091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 02/09/2024] [Accepted: 03/07/2024] [Indexed: 04/11/2024]
Abstract
Accurate assessment of cerebral perfusion is vital for understanding the hemodynamic processes involved in various neurological disorders and guiding clinical decision-making. This guidelines article provides a comprehensive overview of quantitative perfusion imaging of the brain using multi-timepoint arterial spin labeling (ASL), along with recommendations for its acquisition and quantification. A major benefit of acquiring ASL data with multiple label durations and/or post-labeling delays (PLDs) is being able to account for the effect of variable arterial transit time (ATT) on quantitative perfusion values and additionally visualize the spatial pattern of ATT itself, providing valuable clinical insights. Although multi-timepoint data can be acquired in the same scan time as single-PLD data with comparable perfusion measurement precision, its acquisition and postprocessing presents challenges beyond single-PLD ASL, impeding widespread adoption. Building upon the 2015 ASL consensus article, this work highlights the protocol distinctions specific to multi-timepoint ASL and provides robust recommendations for acquiring high-quality data. Additionally, we propose an extended quantification model based on the 2015 consensus model and discuss relevant postprocessing options to enhance the analysis of multi-timepoint ASL data. Furthermore, we review the potential clinical applications where multi-timepoint ASL is expected to offer significant benefits. This article is part of a series published by the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group, aiming to guide and inspire the advancement and utilization of ASL beyond the scope of the 2015 consensus article.
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Affiliation(s)
- Joseph G Woods
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Eric Achten
- Ghent Institute for Functional and Metabolic Imaging (GIfMI), Ghent University, Ghent, Belgium
| | - Iris Asllani
- Department of Neuroscience, University of Sussex, Brighton, UK
- Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, New York, USA
| | - Divya S Bolar
- Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Weiying Dai
- Department of Computer Science, State University of New York at Binghamton, Binghamton, New York, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Audrey P Fan
- Department of Biomedical Engineering, University of California Davis, Davis, California, USA
- Department of Neurology, University of California Davis, Davis, California, USA
| | - María A Fernández-Seara
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | - Xavier Golay
- UCL Queen Square Institute of Neurology, University College London, London, UK
- Gold Standard Phantoms, Sheffield, UK
| | - Matthias Günther
- Imaging Physics, Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- Department of Physics and Electrical Engineering, University of Bremen, Bremen, Germany
| | - Jia Guo
- Department of Bioengineering, University of California Riverside, Riverside, California, USA
| | | | - Mai-Lan Ho
- Department of Radiology, University of Missouri, Columbia, Missouri, USA
| | - Meher R Juttukonda
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Bradley J MacIntosh
- Hurvitz Brain Sciences Program, Centre for Brain Resilience & Recovery, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Computational Radiology & Artificial Intelligence unit, Oslo University Hospital, Oslo, Norway
| | - Ananth J Madhuranthakam
- Department of Radiology and Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Henk-Jan Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | - Thomas W Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Laura M Parkes
- School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Manchester, UK
| | - Nandor Pinter
- Dent Neurologic Institute, Buffalo, New York, USA
- University at Buffalo Neurosurgery, Buffalo, New York, USA
| | - Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Marion Smits
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Medical Delta, Delft, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Rotterdam, The Netherlands
| | - Yuriko Suzuki
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Matthias J P Van Osch
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Danny J J Wang
- Laboratory of FMRI Technology (LOFT), Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, California, USA
| | - Esther A H Warnert
- Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Erasmus MC Cancer Institute, Erasmus MC, Rotterdam, The Netherlands
| | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Fernando Zelaya
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Moss Zhao
- Department of Radiology, Stanford University, Stanford, California, USA
- Maternal & Child Health Research Institute, Stanford University, Stanford, California, USA
| | - Michael A Chappell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
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Cooke GS, Flower B, Cunningham E, Marshall AD, Lazarus JV, Palayew A, Jia J, Aggarwal R, Al-Mahtab M, Tanaka Y, Jeong SH, Poovorawan K, Waked I, Hiebert L, Khue PM, Grebely J, Alcantara-Payawal D, Sanchez-Avila JF, Mbendi C, Muljono DH, Lesi O, Desalegn H, Hamid S, de Araujo A, Cheinquer H, Onyekwere CA, Malyuta R, Ivanchuk I, Thomas DL, Pimenov N, Chulanov V, Dirac MA, Han H, Ward JW. Progress towards elimination of viral hepatitis: a Lancet Gastroenterology & Hepatology Commission update. Lancet Gastroenterol Hepatol 2024; 9:346-365. [PMID: 38367629 DOI: 10.1016/s2468-1253(23)00321-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 09/11/2023] [Accepted: 09/14/2023] [Indexed: 02/19/2024]
Abstract
The top 20 highest burdened countries (in disability-adjusted life years) account for more than 75% of the global burden of viral hepatitis. An effective response in these 20 countries is crucial if global elimination targets are to be achieved. In this update of the Lancet Gastroenterology & Hepatology Commission on accelerating the elimination of viral hepatitis, we convene national experts from each of the top 20 highest burdened countries to provide an update on progress. Although the global burden of diseases is falling, progress towards elimination varies greatly by country. By use of a hepatitis elimination policy index conceived as part of the 2019 Commission, we measure countries' progress towards elimination. Progress in elimination policy has been made in 14 of 20 countries with the highest burden since 2018, with the most substantial gains observed in Bangladesh, India, Indonesia, Japan, and Russia. Most improvements are attributable to the publication of formalised national action plans for the elimination of viral hepatitis, provision of publicly funded screening programmes, and government subsidisation of antiviral treatments. Key themes that emerged from discussion between national commissioners from the highest burdened countries build on the original recommendations to accelerate the global elimination of viral hepatitis. These themes include the need for simplified models of care, improved access to appropriate diagnostics, financing initiatives, and rapid implementation of lessons from the COVID-19 pandemic.
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Affiliation(s)
- Graham S Cooke
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA.
| | - Barnaby Flower
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, London, UK
| | | | | | - Jeffrey V Lazarus
- CUNY Graduate School of Public Health and Health Policy, New York, NY, USA; Barcelona Institute for Global Health, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Adam Palayew
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Jidong Jia
- Liver Research Centre, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Rakesh Aggarwal
- Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Mamum Al-Mahtab
- Department of Hepatology, Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh
| | - Yashuito Tanaka
- Department of Gastroenterology and Hepatology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | - Sook-Hyang Jeong
- Department of Internal Medicine, Seoul National University Bundang Hospital, College of Medicine, Seoul National University, Seongnam, South Korea
| | - Kittiyod Poovorawan
- Department of Clinical Tropical Medicine, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand; Mahidol Oxford Research Unit, Faculty of Tropical Medicine, Mahidol University, Thailand
| | - Imam Waked
- Hepatology Department, National Liver Institute, Shibin El Kom, Egypt
| | - Lindsey Hiebert
- Coalition for Global Hepatitis Elimination, Task Force for Global Health, Decatur, GA, USA
| | - Pham M Khue
- Faculty of Public Health, Haiphong University of Medicine and Pharmacy, Haiphong, Viet Nam
| | - Jason Grebely
- The Kirby Institute, UNSW Sydney, Sydney, NSW, Australia
| | - Diana Alcantara-Payawal
- Department of Internal Medicine, Fatima University Medical Center, Valenzuela, Philippines; Committee on Hepatology, Section of Gastroenterology, Cardinal Santos Medical Center, San Juan, Philippines
| | - Juan F Sanchez-Avila
- Global Health and Emerging Diseases Investigation Group, Escuela de Medicina y Ciencias de la Salud, Tecnologico de Monterrey Monterrey, Mexico
| | - Charles Mbendi
- Service of Gastroenterology, Internal Medicine, University Clinic of Kinshasa, Faculty of Medicine, University of Kinshasa, Kinshasha, DR Congo
| | - David H Muljono
- Ministry of Health, Jakarta, Indonesia; Faculty of Medicine, Universitas Hasanuddin, Makassar, Indonesia; Indonesian Academy of Sciences, Jakarta, Indonesia
| | - Olufunmilayo Lesi
- Gastroenterology and Hepatology Unit, College of Medicine, University of Lagos and Lagos University Teaching Hospital, Lagos, Nigeria
| | - Hailemichael Desalegn
- Department of Internal Medicine, St Paul's Hospital Millennium Medical College, Addis Ababa, Ethiopia
| | - Saeed Hamid
- Clinical Trials Unit, Aga Khan University, Karachi, Pakistan
| | - Alexandre de Araujo
- Universidade Federal do Rio Grande do Sul, Gastroenterology and Hepatology Unit of Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
| | - Hugo Cheinquer
- Universidade Federal do Rio Grande do Sul, Gastroenterology and Hepatology Unit of Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
| | - Charles A Onyekwere
- Deparment Of Medicine, Lagos State University College of Medicine, Lagos, Nigeria
| | | | - Iryna Ivanchuk
- Department of Viral Hepatitis Control at National Institute of Public Health, Kyiv, Ukraine
| | - David L Thomas
- Divison of Infectious Diseases, John Hopkins School of Medicine, Baltimore, MD, USA
| | - Nikolay Pimenov
- National Medical Research Center of Tuberculosis and Infectious Diseases, Moscow, Russia
| | | | - Mae Ashworth Dirac
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, University of Washington, Seattle, WA, USA; Department of Family Medicine, University of Washington, Seattle, WA, USA
| | - Hannah Han
- Department of Epidemiology, University of Washington, Seattle, WA, USA; Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - John W Ward
- Coalition for Global Hepatitis Elimination, Task Force for Global Health, Decatur, GA, USA; Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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Falade-Nwulia O, Lesko CR, Fojo AT, Keruly JC, Moore RD, Sutcliffe CG, Mehta SH, Chander G, Thomas DL, Sulkowski M. Hepatitis C Treatment in People With HIV: Potential to Eliminate Disease and Disparity. J Infect Dis 2024; 229:775-779. [PMID: 37793170 PMCID: PMC10938212 DOI: 10.1093/infdis/jiad433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 09/20/2023] [Accepted: 10/02/2023] [Indexed: 10/06/2023] Open
Abstract
Access to direct acting antivirals (DAAs) may be associated with reductions in hepatitis C virus (HCV) viremia prevalence among people with human immunodeficiency virus (PWH). Among 3755 PWH, estimated HCV viremia prevalence decreased by 94.0% from 36% (95% confidence interval [CI], 27%-46%) in 2009 (pre-DAA era) to 2% (95% CI, 0%-4%) in 2021 (DAA era). Male sex, black race, and older age were associated with HCV viremia in 2009 but not in 2021. Injection drug use remained associated with HCV viremia in 2009 and 2021. Targeted interventions are needed to meet the HCV care needs of PWH who use drugs.
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Affiliation(s)
- Oluwaseun Falade-Nwulia
- Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Catherine R Lesko
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Anthony T Fojo
- Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jeanne C Keruly
- Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Richard D Moore
- Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Catherine G Sutcliffe
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Shruti H Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Geetanjali Chander
- Division of General Internal Medicine, University of Washington, Seattle, Washington, USA
| | - David L Thomas
- Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mark Sulkowski
- Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Padrela B, Mahroo A, Tee M, Sneve MH, Moyaert P, Geier O, Kuijer JPA, Beun S, Nordhøy W, Zhu YD, Buck MA, Hoinkiss DC, Konstandin S, Huber J, Wiersinga J, Rikken R, de Leeuw D, Grydeland H, Tippett L, Cawston EE, Ozturk-Isik E, Linn J, Brandt M, Tijms BM, van de Giessen EM, Muller M, Fjell A, Walhovd K, Bjørnerud A, Pålhaugen L, Selnes P, Clement P, Achten E, Anazodo U, Barkhof F, Hilal S, Fladby T, Eickel K, Morgan C, Thomas DL, Petr J, Günther M, Mutsaerts HJMM. Developing blood-brain barrier arterial spin labelling as a non-invasive early biomarker of Alzheimer's disease (DEBBIE-AD): a prospective observational multicohort study protocol. BMJ Open 2024; 14:e081635. [PMID: 38458785 DOI: 10.1136/bmjopen-2023-081635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/10/2024] Open
Abstract
INTRODUCTION Loss of blood-brain barrier (BBB) integrity is hypothesised to be one of the earliest microvascular signs of Alzheimer's disease (AD). Existing BBB integrity imaging methods involve contrast agents or ionising radiation, and pose limitations in terms of cost and logistics. Arterial spin labelling (ASL) perfusion MRI has been recently adapted to map the BBB permeability non-invasively. The DEveloping BBB-ASL as a non-Invasive Early biomarker (DEBBIE) consortium aims to develop this modified ASL-MRI technique for patient-specific and robust BBB permeability assessments. This article outlines the study design of the DEBBIE cohorts focused on investigating the potential of BBB-ASL as an early biomarker for AD (DEBBIE-AD). METHODS AND ANALYSIS DEBBIE-AD consists of a multicohort study enrolling participants with subjective cognitive decline, mild cognitive impairment and AD, as well as age-matched healthy controls, from 13 cohorts. The precision and accuracy of BBB-ASL will be evaluated in healthy participants. The clinical value of BBB-ASL will be evaluated by comparing results with both established and novel AD biomarkers. The DEBBIE-AD study aims to provide evidence of the ability of BBB-ASL to measure BBB permeability and demonstrate its utility in AD and AD-related pathologies. ETHICS AND DISSEMINATION Ethics approval was obtained for 10 cohorts, and is pending for 3 cohorts. The results of the main trial and each of the secondary endpoints will be submitted for publication in a peer-reviewed journal.
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Affiliation(s)
- Beatriz Padrela
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | - Amnah Mahroo
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Mervin Tee
- National University Health System, Singapore
| | - Markus H Sneve
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Paulien Moyaert
- Lawson Health Research Institute, London, Ontario, Canada
- Department of Diagnostic Sciences, University Hospital Ghent, Gent, Belgium
| | - Oliver Geier
- Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Joost P A Kuijer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | - Soetkin Beun
- Department of Diagnostic Sciences, University Hospital Ghent, Gent, Belgium
| | - Wibeke Nordhøy
- Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Yufei David Zhu
- Biomedical Engineering, University of California Davis, Davis, California, USA
| | - Mareike A Buck
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- University of Bremen, Bremen, Germany
| | | | - Simon Konstandin
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Jörn Huber
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
| | - Julia Wiersinga
- Department of Internal Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | - Roos Rikken
- Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | | | - Håkon Grydeland
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
| | - Lynette Tippett
- The University of Auckland School of Psychology, Auckland, New Zealand
| | - Erin E Cawston
- The University of Auckland Department of Pharmacology and Clinical Pharmacology, Auckland, New Zealand
| | - Esin Ozturk-Isik
- Bogazici University Institute of Biomedical Engineering, Istanbul, Turkey
| | - Jennifer Linn
- Department of Neurology, Faculty of Medicine, Babylon, Iraq
- Department of Neurology, Technische Universität Dresden, Dresden, Germany
| | - Moritz Brandt
- Department of Neurology, Faculty of Medicine, Babylon, Iraq
- Department of Neurology, Technische Universität Dresden, Dresden, Germany
| | - Betty M Tijms
- Neurology, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | | | - Majon Muller
- Department of Internal Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
| | - Anders Fjell
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | - Kristine Walhovd
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | - Atle Bjørnerud
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Oslo, Norway
- Oslo University Hospital, Oslo, Norway
| | - Lene Pålhaugen
- Department of Neurology, Akershus University Hospital, Lorenskog, Norway
- University of Oslo, Oslo, Norway
| | - Per Selnes
- Department of Neurology, Akershus University Hospital, Lorenskog, Norway
| | - Patricia Clement
- Department of Diagnostic Sciences, University Hospital Ghent, Gent, Belgium
| | - Eric Achten
- Department of Diagnostic Sciences, University Hospital Ghent, Gent, Belgium
| | - Udunna Anazodo
- Lawson Health Research Institute, London, Ontario, Canada
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
- University College London, London, UK
| | - Saima Hilal
- National University Health System, Singapore
- Department of Pharmacology, National University of Singapore, Singapore
| | - Tormod Fladby
- Department of Neurology, Akershus University Hospital, Lorenskog, Norway
- University of Oslo, Oslo, Norway
| | - Klaus Eickel
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- University of Applied Sciences Bremerhaven, Bremerhaven, Germany
| | - Catherine Morgan
- The University of Auckland School of Psychology, Auckland, New Zealand
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, University College London, London, UK
| | - Jan Petr
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
- Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Matthias Günther
- Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany
- University of Bremen, Bremen, Germany
| | - Henk J M M Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Locatie VUmc, Amsterdam, Netherlands
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11
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Demko ZO, Yu T, Mullapudi SK, Varela Heslin MG, Dorsey CA, Payton CB, Tornheim JA, Blair PW, Mehta SH, Thomas DL, Manabe YC, Antar AAR. Two-Year Longitudinal Study Reveals That Long COVID Symptoms Peak and Quality of Life Nadirs at 6-12 Months Postinfection. Open Forum Infect Dis 2024; 11:ofae027. [PMID: 38449921 PMCID: PMC10917418 DOI: 10.1093/ofid/ofae027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 01/11/2024] [Indexed: 03/08/2024] Open
Abstract
Background Few longitudinal studies available characterize long COVID outcomes out to 24 months, especially in people with nonsevere acute coronavirus disease 2019 (COVID-19). This study sought to prospectively characterize incidence and duration of long COVID symptoms and their association with quality of life (QoL) from 1-24 months after mild-to-moderate COVID-19 using validated tools in a diverse cohort of unvaccinated people infected with SARS-CoV-2 in 2020. Methods At 1-3, 6, 12, 18, and 24 months post-COVID-19, 70 participants had orthostatic vital signs measured, provided blood, and completed surveys characterizing symptoms, QoL, and return to pre-COVID-19 health and activities using validated tools (FLU-PRO+, Fatigue Severity Scale, Insomnia Severity Index, General Practitioner Assessment of Cognition, Patient Health Questionnaire Depression 8-Item, Generalized Anxiety Disorder 7-Item, 36-Item Short-Form Health Survey, EuroQol EQ-5D-5L). Results During the study period, 33% of participants experienced long COVID (had not returned to pre-COVID-19 health status and reported at least 1 symptom >90 days postinfection); 8% had not returned to their pre-COVID-19 health status 24 months postinfection. Long COVID symptoms peaked 6 months post-COVID-19, frequently causing activity limitations. Having long COVID was significantly associated with decreased QoL in multiple domains. Frequencies of orthostatic hypotension and tachycardia reflected levels reported in the general population. Within-person weight increased significantly between months 1 and 6. Long COVID was associated with pre-COVID-19 obesity and hyperlipidemia, but not with high-sensitivity C-reactive protein levels 1-3 months postinfection. Conclusions Long COVID occurs in a significant proportion of unvaccinated people, even if the acute illness was not severe. Long COVID prevalence peaked 6-12 months post-COVID-19, and a small proportion of participants still reported not returning to their pre-COVID-19 health status 24 months post-COVID-19.
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Affiliation(s)
- Zoe O Demko
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Tong Yu
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Sarika K Mullapudi
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Chamia A Dorsey
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Christine B Payton
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jeffrey A Tornheim
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Paul W Blair
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Austere Environments Consortium for Enhanced Sepsis Outcomes, Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, Maryland, USA
| | - Shruti H Mehta
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - David L Thomas
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yukari C Manabe
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Annukka A R Antar
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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12
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Li H, Daniel AJ, Buchanan CE, Nery F, Morris DM, Li S, Huang Y, Sousa JA, Sourbron S, Mendichovszky IA, Thomas DL, Priest AN, Francis ST. Improvements in Between-Vendor MRI Harmonization of Renal T 2 Mapping using Stimulated Echo Compensation. J Magn Reson Imaging 2024. [PMID: 38380700 DOI: 10.1002/jmri.29282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND T2 mapping is valuable to evaluate pathophysiology in kidney disease. However, variations in T2 relaxation time measurements across MR scanners and vendors may occur requiring additional correction. PURPOSE To harmonize renal T2 measurements between MR vendor platforms, and use an extended-phase-graph-based fitting method ("StimFit") to correct stimulated echoes and reduce between-vendor variations. STUDY TYPE Prospective. SUBJECTS 8 healthy "travelling" volunteers (37.5% female, 32 ± 6 years) imaged on four MRI systems across three vendors at four sites, 10 healthy volunteers (50% female, 32 ± 8 years) scanned multiple times on a given MR scanner for repeatability evaluation. ISMRM/NIST system phantom scanned for evaluation of T2 accuracy. FIELD STRENGTH/SEQUENCE 3T, multiecho spin-echo sequence. ASSESSMENT T2 images fit using conventional monoexponential fitting and "StimFit." Mean absolute percentage error (MAPE) of phantom measurements with reference T2 values. Average cortex and medulla T2 values compared between MR vendors, with masks obtained from T2 -weighted images and T1 maps. Full-width-at-half-maximum (FWHM) T2 distributions to evaluate local homogeneity of measurements. STATISTICAL TESTS Coefficient of variation (CV), linear mixed-effects model, analysis of variance, student's t-tests, Bland-Altman plots, P-value <0.05 considered statistically significant. RESULTS In the ISMRM/NIST phantom, "StimFit" reduced the MAPE from 4.9%, 9.1%, 24.4%, and 18.1% for the four sites (three vendors) to 3.3%, 3.0%, 6.6%, and 4.1%, respectively. In vivo, there was a significant difference in kidney T2 measurements between vendors using a monoexponential fit, but not with "StimFit" (P = 0.86 and 0.92, cortex and medulla, respectively). The intervendor CVs of T2 measures were reduced from 8.0% to 2.6% (cortex) and 7.1% to 2.8% (medulla) with StimFit, resulting in no significant differences for the CVs of intravendor repeat acquisitions (P = 0.13 and 0.05). "StimFit" significantly reduced the FWHM of T2 distributions in the cortex and whole kidney. DATA CONCLUSION Stimulated-echo correction reduces renal T2 variation across MR vendor platforms. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Hao Li
- The Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Alexander J Daniel
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | | | - Fábio Nery
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, UK
| | - David M Morris
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Shaohang Li
- The Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Yuan Huang
- Department of Radiology, University of Cambridge, Cambridge, UK
- EPSRC Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, UK
| | - João A Sousa
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Steven Sourbron
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Iosif A Mendichovszky
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - David L Thomas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew N Priest
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and School of Medicine, Nottingham, UK
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13
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Murray-Smith H, Barker S, Barkhof F, Barnes J, Brown TM, Captur G, R E Cartlidge M, Cash DM, Coath W, Davis D, Dickson JC, Groves J, Hughes AD, James SN, Keshavan A, Keuss SE, King-Robson J, Lu K, Malone IB, Nicholas JM, Rapala A, Scott CJ, Street R, Sudre CH, Thomas DL, Wong A, Wray S, Zetterberg H, Chaturvedi N, Fox NC, Crutch SJ, Richards M, Schott JM. Updating the study protocol: Insight 46 - a longitudinal neuroscience sub-study of the MRC National Survey of Health and Development - phases 2 and 3. BMC Neurol 2024; 24:40. [PMID: 38263061 PMCID: PMC10804658 DOI: 10.1186/s12883-023-03465-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 11/13/2023] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Although age is the biggest known risk factor for dementia, there remains uncertainty about other factors over the life course that contribute to a person's risk for cognitive decline later in life. Furthermore, the pathological processes leading to dementia are not fully understood. The main goals of Insight 46-a multi-phase longitudinal observational study-are to collect detailed cognitive, neurological, physical, cardiovascular, and sensory data; to combine those data with genetic and life-course information collected from the MRC National Survey of Health and Development (NSHD; 1946 British birth cohort); and thereby contribute to a better understanding of healthy ageing and dementia. METHODS/DESIGN Phase 1 of Insight 46 (2015-2018) involved the recruitment of 502 members of the NSHD (median age = 70.7 years; 49% female) and has been described in detail by Lane and Parker et al. 2017. The present paper describes phase 2 (2018-2021) and phase 3 (2021-ongoing). Of the 502 phase 1 study members who were invited to a phase 2 research visit, 413 were willing to return for a clinic visit in London and 29 participated in a remote research assessment due to COVID-19 restrictions. Phase 3 aims to recruit 250 study members who previously participated in both phases 1 and 2 of Insight 46 (providing a third data time point) and 500 additional members of the NSHD who have not previously participated in Insight 46. DISCUSSION The NSHD is the oldest and longest continuously running British birth cohort. Members of the NSHD are now at a critical point in their lives for us to investigate successful ageing and key age-related brain morbidities. Data collected from Insight 46 have the potential to greatly contribute to and impact the field of healthy ageing and dementia by combining unique life course data with longitudinal multiparametric clinical, imaging, and biomarker measurements. Further protocol enhancements are planned, including in-home sleep measurements and the engagement of participants through remote online cognitive testing. Data collected are and will continue to be made available to the scientific community.
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Affiliation(s)
- Heidi Murray-Smith
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK.
| | - Suzie Barker
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
- Centre for Medical Image Computing, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Josephine Barnes
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Thomas M Brown
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Gabriella Captur
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Molly R E Cartlidge
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - David M Cash
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - William Coath
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Daniel Davis
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - John C Dickson
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - James Groves
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Alun D Hughes
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Sarah E Keuss
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Josh King-Robson
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Kirsty Lu
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Ian B Malone
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Jennifer M Nicholas
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Alicja Rapala
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Catherine J Scott
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
- Institute of Nuclear Medicine, University College London Hospitals, London, UK
| | - Rebecca Street
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Carole H Sudre
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
- Centre for Medical Image Computing, University College London, London, UK
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - David L Thomas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Selina Wray
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Henrik Zetterberg
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute, University College London, London, UK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Hong, Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science & Experimental Medicine, UCL Institute of Cardiovascular Science, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, 1St Floor, 8-11 Queen Square, London, UK
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14
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Traut CC, Jones JL, Sanders RA, Clark LR, Hamill MM, Stavrakis G, Sop J, Beckey TP, Keller SC, Gilliams EA, Cochran WV, Laeyendecker O, Manabe YC, Mostafa HH, Thomas DL, Hansoti B, Gebo KA, Blankson JN. Orthopoxvirus-Specific T-Cell Responses in Convalescent Mpox Patients. J Infect Dis 2024; 229:54-58. [PMID: 37380166 PMCID: PMC10786252 DOI: 10.1093/infdis/jiad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/07/2023] [Accepted: 06/24/2023] [Indexed: 06/30/2023] Open
Abstract
Orthopoxvirus-specific T-cell responses were analyzed in 10 patients who had recovered from Mpox including 7 people with human immunodeficiency virus (PWH). Eight participants had detectable virus-specific T-cell responses, including a PWH who was not on antiretroviral therapy and a PWH on immunosuppressive therapy. These 2 participants had robust polyfunctional CD4+ T-cell responses to peptides from the 121L vaccinia virus (VACV) protein. T-cells from 4 of 5 HLA-A2-positive participants targeted at least 1 previously described HLA-A2-restricted VACV epitope, including an epitope targeted in 2 participants. These results advance our understanding of immunity in convalescent Mpox patients.
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Affiliation(s)
- Caroline C Traut
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Joyce L Jones
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Renata A Sanders
- Department of Pediatrics, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Laura R Clark
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Matthew M Hamill
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Georgia Stavrakis
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Joel Sop
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Tyler P Beckey
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Sara C Keller
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | | | - Willa V Cochran
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Oliver Laeyendecker
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
- Intramural Research Program, National Institute of Allergy and Infectious Diseases, Bethesda, Maryland, USA
| | - Yukari C Manabe
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Heba H Mostafa
- Department of Pathology, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - David L Thomas
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Bhakti Hansoti
- Department of Emergency Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Kelly A Gebo
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
| | - Joel N Blankson
- Department of Medicine, Johns Hopkins Medicine, Baltimore, Maryland, USA
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15
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Keuss SE, Coath W, Cash DM, Barnes J, Nicholas JM, Lane CA, Parker TD, Keshavan A, Buchanan SM, Wagen AZ, Storey M, Harris M, Lu K, James SN, Street R, Malone IB, Sudre CH, Thomas DL, Dickson JC, Barkhof F, Murray-Smith H, Wong A, Richards M, Fox NC, Schott JM. Rates of cortical thinning in Alzheimer's disease signature regions associate with vascular burden but not with β-amyloid status in cognitively normal adults at age 70. J Neurol Neurosurg Psychiatry 2024:jnnp-2023-332067. [PMID: 38199813 DOI: 10.1136/jnnp-2023-332067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 12/18/2023] [Indexed: 01/12/2024]
Abstract
BACKGROUND Consistent patterns of reduced cortical thickness have been identified in early Alzheimer's disease (AD). However, the pathological factors that influence rates of cortical thinning within these AD signature regions remain unclear. METHODS Participants were from the Insight 46 substudy of the MRC National Survey of Health and Development (NSHD; 1946 British birth cohort), a prospective longitudinal cohort study. Linear regression was used to examine associations of baseline cerebral β-amyloid (Aβ) deposition, measured using florbetapir positron emission tomography, and baseline white matter hyperintensity volume (WMHV) on MRI, a marker of cerebral small vessel disease, with subsequent longitudinal changes in AD signature cortical thickness quantified from baseline and repeat MRI (mean [SD] interval 2.4 [0.2] years). RESULTS In a population-based sample of 337 cognitively normal older white adults (mean [SD] age at baseline 70.5 [0.6] years; 48.1% female), higher global WMHV at baseline related to faster subsequent rates of cortical thinning in both AD signature regions (~0.15%/year faster per 10 mL additional WMHV), whereas baseline Aβ status did not. Among Aβ positive participants (n=56), there was some evidence that greater global Aβ standardised uptake value ratio at baseline related to faster cortical thinning in the AD signature Mayo region, but this did not reach statistical significance (p=0.08). CONCLUSIONS Cortical thinning within AD signature regions may develop via cerebrovascular pathways. Perhaps reflecting the age of the cohort and relatively low prevalence of Aβ-positivity, robust Aβ-related differences were not detected. Longitudinal follow-up incorporating additional biomarkers will allow assessment of how these relationships evolve closer to expected dementia onset.
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Affiliation(s)
- Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Dementia Research Institute, University College London, London, UK
| | - Josephine Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Aaron Z Wagen
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Neurodegeneration Biology Laboratory, The Francis Crick Institute, London, UK
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Mathew Storey
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Matthew Harris
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah-Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Rebecca Street
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Carole H Sudre
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Centre for Medical Imaging Computing, University College London, London, UK
| | - David L Thomas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Brain Repair and Neurorehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - John C Dickson
- Institute of Nuclear Medicine, University College London Hospitals NHS Foundation Trust, London, UK
| | - Frederik Barkhof
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Centre for Medical Imaging Computing, University College London, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centre, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Heidi Murray-Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
- Dementia Research Institute, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
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16
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Balagopal A, Thomas DL. Genomics on the road to functional cure of hepatitis B. J Hepatol 2024; 80:6-7. [PMID: 37984710 DOI: 10.1016/j.jhep.2023.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/13/2023] [Accepted: 11/13/2023] [Indexed: 11/22/2023]
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17
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Coyle CR, Gicquelais RE, Genberg BL, Astemborski J, Falade-Nwulia O, Kirk GD, Thomas DL, Mehta SH. Temporal trends in HCV treatment uptake and success among people who inject drugs in Baltimore, MD since the introduction of direct acting antivirals. Drug Alcohol Depend 2023; 253:111007. [PMID: 38456165 PMCID: PMC10917145 DOI: 10.1016/j.drugalcdep.2023.111007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Background Although hepatitis C virus (HCV) can be cured by direct acting antivirals (DAA), uptake is not well characterized for people who inject drugs (PWID). Methods Among 1,130 participants of a community-based cohort of PWID with chronic HCV, we longitudinally characterized HCV treatment uptake and cure early (2014-2016) and later (2017-2020). Results Cumulative HCV treatment uptake increased from 4% in 2014 to 68% in 2020 and the percent with HCV viremia declined from nearly 100% to 33%. Predictors of treatment uptake varied across periods. Age (incidence rate ratio [IRR] per 5-year increase: 1.28; 95% confidence interval [CI]: 1.15, 1.42), educational attainment (IRR for ≥ high school diploma: 1.31; 95% CI: 1.04, 1.66), HIV coinfection with suppressed viral load (IRR vs. HIV negative: 2.08; 95% CI: 1.63, 2.66) and alcohol dependence (IRR vs. no alcohol use: 0.63; 95% CI: 0.43, 0.91) were associated with treatment uptake in the early period, but not later. HIV coinfection with a detectable viral load (IRR vs. HIV negative: 0.46; 95% CI: 0.23, 0.95) and daily injecting (IRR: 0.46 vs. no injection; 95% CI: 0.27, 0.79) were significantly associated with lower treatment uptake later. Homelessness was associated with significantly reduced likelihood of viral clearance in the late DAA era (IRR: 0.51; 95% CI: 0.30, 0.88). Conclusion Treatment uptake improved substantially in this cohort of PWID in the first five years of DAA availability with commensurate declines in viremia. Additional efforts are needed to treat those actively injecting and unstably housed in order to realize elimination goals.
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Affiliation(s)
- Catelyn R. Coyle
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, United States of America
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, United States of America
- Center for Observational and Real-World Evidence (CORE), Merck & Co, Inc, 351 N Sumneytown Pike, North Wales, PA 19454, United States of America
| | - Rachel E. Gicquelais
- School of Nursing, University of Wisconsin-Madison, 701 Highland Ave, Madison, WI 53705, United States of America
| | - Becky L. Genberg
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, United States of America
| | - Jacquie Astemborski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, United States of America
| | - Oluwaseun Falade-Nwulia
- Division of Infectious Disease, Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD 21205, United States of America
| | - Gregory D. Kirk
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, United States of America
- Division of Infectious Disease, Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD 21205, United States of America
| | - David L. Thomas
- Division of Infectious Disease, Johns Hopkins University School of Medicine, 733 N Broadway, Baltimore, MD 21205, United States of America
| | - Shruti H. Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, United States of America
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18
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Alter HJ, Barnes E, Biondi MJ, Cox AL, Eberts JD, Feld JJ, Liang TJ, Morrison J, Rice CM, Shoukry NH, Thomas DL, Van Gennip J, Weijer C. Joint statement in support of hepatitis C human challenge studies. Lancet Gastroenterol Hepatol 2023; 8:967-969. [PMID: 37742699 DOI: 10.1016/s2468-1253(23)00314-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/07/2023] [Accepted: 09/08/2023] [Indexed: 09/26/2023]
Affiliation(s)
- Harvey J Alter
- Transfusion Medicine Department, National Institutes of Health Clinical Center, Bethesda, MD, USA
| | - Eleanor Barnes
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Mia J Biondi
- School of Nursing, York University, Toronto, ON, Canada
| | - Andrea L Cox
- The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jake D Eberts
- 1Day Sooner, Washington, DC, 20002, USA, University of Toronto, Toronto, ON, Canada.
| | - Jordan J Feld
- Toronto Centre for Liver Disease, Toronto General Hospital, Toronto, ON, Canada
| | - T Jake Liang
- Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | | | - Charles M Rice
- Laboratory of Virology and Infectious Disease, Rockefeller University, New York, NY, USA; Departments of Medicine and of Microbiology and Immunology, Université de Montréal, Montréal, QC, Canada
| | - Naglaa H Shoukry
- Laboratory of Liver Immunology, University of Montreal Hospital Research Centre (CRCHUM), Montréal, QC, Canada
| | - David L Thomas
- The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Charles Weijer
- Departments of Philosophy, Medicine, and Epidemiology and Biostatistics, Western University, London, ON, Canada
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19
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Coyle CR, Desjardins MR, Curriero FC, Rudolph J, Astemborski J, Falade-Nwulia O, Kirk GD, Thomas DL, Mehta SH, Genberg BL. Geographic variation in HCV treatment penetration among people who inject drugs in Baltimore, MD. J Viral Hepat 2023; 30:810-818. [PMID: 37382024 PMCID: PMC10527489 DOI: 10.1111/jvh.13864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/06/2023] [Accepted: 06/09/2023] [Indexed: 06/30/2023]
Abstract
We evaluated geographic heterogeneity in hepatitis C virus (HCV) treatment penetration among people who inject drug (PWID) across Baltimore, MD since the advent of direct-acting antivirals (DAAs) using space-time clusters of HCV viraemia. Using data from a community-based cohort of PWID, the AIDS Linked to the IntraVenous Experience (ALIVE) study, we identified space-time clusters with higher-than-expected rates of HCV viraemia between 2015 and 2019 using scan statistics. We used Poisson regression to identify covariates associated with HCV viraemia and used the regression-fitted values to detect adjusted space-time clusters of HCV viraemia in Baltimore city. Overall, in the cohort, HCV viraemia fell from 77% in 2015 to 64%, 49%, 39% and 36% from 2016 to 2019. In Baltimore city, the percentage of census tracts where prevalence of HCV viraemia was ≥85% dropped from 57% to 34%, 25%, 22% and 10% from 2015 to 2019. We identified two clusters of higher-than-expected HCV viraemia in the unadjusted analysis that lasted from 2015 to 2017 in East and West Baltimore and one adjusted cluster of HCV viraemia in West Baltimore from 2015 to 2016. Neither differences in age, sex, race, HIV status, nor neighbourhood deprivation were able to explain the significant space-time clusters. However, residing in a cluster with higher-than-expected viraemia was associated with age, sex, educational attainment and higher levels of neighbourhood deprivation. Nearly 4 years after DAAs became available, HCV treatment has penetrated all PWID communities across Baltimore city. While nearly all census tracts experienced improvements, change was more gradual in areas with higher levels of poverty.
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Affiliation(s)
- Catelyn R. Coyle
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Merck & Co. Inc., Rahway, NJ
| | - Michael R. Desjardins
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Frank C. Curriero
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Jacqueline Rudolph
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Jacquie Astemborski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Oluwaseun Falade-Nwulia
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Gregory D. Kirk
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - David L. Thomas
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Shruti H. Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Becky L. Genberg
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
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20
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Liang TJ, Feld JJ, Shoukry NH, Thomas DL. Controlled Human Infection Model for Hepatitis C Virus Vaccine Development: Is It Time to Be Real? Clin Infect Dis 2023; 77:S215. [PMID: 37579205 PMCID: PMC10893913 DOI: 10.1093/cid/ciad343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023] Open
Affiliation(s)
- T Jake Liang
- Liver Diseases Branch, National Institute of Diabetics and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Jordan J Feld
- Toronto Centre for Liver Disease, Toronto General Hospital, University Health Network, Toronto, Canada
- The Toronto Viral Hepatitis Care Network (VIRCAN), Toronto, Canada
| | - Naglaa H Shoukry
- Département de Médecine, Université de Montréal, Montreal, Quebec, Canada
| | - David L Thomas
- Department of Medicine, The Johns Hopkins University, Baltimore, Maryland, USA
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21
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Feld JJ, Bruneau J, Dore GJ, Ghany MG, Hansen B, Sulkowski M, Thomas DL. Controlled Human Infection Model for Hepatitis C Virus Vaccine Development: Trial Design Considerations. Clin Infect Dis 2023; 77:S262-S269. [PMID: 37579209 PMCID: PMC10425135 DOI: 10.1093/cid/ciad362] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023] Open
Abstract
The design of a clinical trial for a controlled human infection model (CHIM) to accelerate hepatitis C virus (HCV) vaccine development requires careful consideration. The design of a potential approach to HCV CHIM is outlined, involving initial sentinel cohorts to establish the safety and curability of the viral inoculum followed by larger cohorts to establish the spontaneous clearance rate for each inoculum. The primary endpoint would be HCV clearance by 24 weeks post-inoculation, recognizing that the prevention of chronic infection would be the primary goal of HCV vaccine candidates. Additional considerations are discussed, including the populations to be enrolled, the required monitoring approach, indications for antiviral therapy, and the required sample size for different CHIM approaches. Finally, safety considerations for CHIM participants are discussed.
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Affiliation(s)
- Jordan J Feld
- Toronto Centre for Liver Disease, University Health Network, University of Toronto, Toronto, Canada
| | - Julie Bruneau
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Université de Montréal, Montreal, Canada
| | - Gregory J Dore
- Kirby Institute, University of New South Wales, Sydney, Australia
| | - Marc G Ghany
- Liver Diseases Branch, National Institutes of Diabetes, Digestive, and Kidney Diseases, Bethesda, Maryland, USA
| | - Bettina Hansen
- Department of Medicine, Erasmus University, Rotterdam, The Netherlands
| | - Mark Sulkowski
- Department of Medicine, The Johns Hopkins University, Baltimore, Maryland, USA
| | - David L Thomas
- Department of Medicine, The Johns Hopkins University, Baltimore, Maryland, USA
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22
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Taso M, Aramendía-Vidaurreta V, Englund EK, Francis S, Franklin S, Madhuranthakam AJ, Martirosian P, Nayak KS, Qin Q, Shao X, Thomas DL, Zun Z, Fernández-Seara MA. Update on state-of-the-art for arterial spin labeling (ASL) human perfusion imaging outside of the brain. Magn Reson Med 2023; 89:1754-1776. [PMID: 36747380 DOI: 10.1002/mrm.29609] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/09/2023] [Accepted: 01/16/2023] [Indexed: 02/08/2023]
Abstract
This review article provides an overview of developments for arterial spin labeling (ASL) perfusion imaging in the body (i.e., outside of the brain). It is part of a series of review/recommendation papers from the International Society for Magnetic Resonance in Medicine (ISMRM) Perfusion Study Group. In this review, we focus on specific challenges and developments tailored for ASL in a variety of body locations. After presenting common challenges, organ-specific reviews of challenges and developments are presented, including kidneys, lungs, heart (myocardium), placenta, eye (retina), liver, pancreas, and muscle, which are regions that have seen the most developments outside of the brain. Summaries and recommendations of acquisition parameters (when appropriate) are provided for each organ. We then explore the possibilities for wider adoption of body ASL based on large standardization efforts, as well as the potential opportunities based on recent advances in high/low-field systems and machine-learning. This review seeks to provide an overview of the current state-of-the-art of ASL for applications in the body, highlighting ongoing challenges and solutions that aim to enable more widespread use of the technique in clinical practice.
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Affiliation(s)
- Manuel Taso
- Division of MRI Research, Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Erin K Englund
- Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Susan Francis
- Sir Peter Mansfield Imaging Center, University of Nottingham, Nottingham, UK
| | - Suzanne Franklin
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Center for Image Sciences, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Ananth J Madhuranthakam
- Department of Radiology, Advanced Imaging Research Center, and Biomedical Engineering, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Petros Martirosian
- Section on Experimental Radiology, Department of Radiology, University Hospital of Tuebingen, Tuebingen, Germany
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Maryland, USA
| | - Xingfeng Shao
- Laboratory of FMRI Technology (LOFT), Mark & Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA
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23
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Nsibirwa SK, Aizire J, Mugerwa JN, Thomas DL, Ocama P, Kirk GD. The impact of HIV infection on clinical presentation and mortality among persons with hepatocellular carcinoma in Kampala, Uganda. BMC Infect Dis 2023; 23:216. [PMID: 37024807 PMCID: PMC10080890 DOI: 10.1186/s12879-023-08164-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 03/15/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND HIV infection is associated with more rapid progression of some comorbidities. This study assessed the impact of HIV-infection on the presentation and outcome of HCC. METHODS HCC patients attending the Mulago National Referral Hospital in Uganda were enrolled into a natural history study of HCC between March 2015 and February 2019. Standardized methods were used to collect clinical, ultrasound and laboratory data at enrolment. HCC cases were confirmed and enrolled based on a combination of clinical, ultrasound, tumor marker and pathology data. Follow-up contact was made at one, three, six, and twelve months post-enrolment to determine vital status. Symptoms and signs at diagnosis and subsequent survival were compared by HIV status. Kaplan Meier curves were used to assess HCC survival. RESULTS Of 441 persons with HCC, 383 (87.0%) died within 12 months following HCC diagnosis. The median (IQR) survival was 42 (20, 106) days. HIV infection was present in 79 (18%) cases. After adjusting for baseline demographic and clinical characteristics, HIV infection was associated with increased mortality but only among those with severe HIV-associated immunosuppression (CD4 count < 200 cells per cubic milliliter), aHR (95% C) = 2.12 (1.23-3.53), p = 0.004, and not among PLWH with ≥ 200 CD4 cells per cubic milliliter, aHR (95% C) = 1.15 (0.82-1.60), p = 0.417. CONCLUSION Among relatively young Ugandans, HCC is a devastating disease with rapid mortality that is especially rapid among people living with HIV(PLWH). HIV was associated with slightly higher mortality, notably among PLWH with lower CD4 cell counts. As a substantial majority of PLWH diagnosed with HCC were engaged in HIV care, further investigation should determine the effectiveness of incorporating screening and early identification of HCC among high-risk individuals into existing HIV care programs. Concurrent with growing access to curative localized treatment for HCC in sub-Saharan Africa, leveraging HIV care infrastructure affords opportunities for earlier HCC intervention.
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Affiliation(s)
- Sara K Nsibirwa
- HIV and HCC in Uganda (H²U) Consortium, Kampala, Uganda.
- Infectious Diseases Institute (IDI), Makerere University, Kampala, Uganda.
| | - Jim Aizire
- HIV and HCC in Uganda (H²U) Consortium, Kampala, Uganda
- Johns Hopkins University, Baltimore, MD, USA
| | | | - David L Thomas
- HIV and HCC in Uganda (H²U) Consortium, Kampala, Uganda
- Johns Hopkins University, Baltimore, MD, USA
| | - Ponsiano Ocama
- HIV and HCC in Uganda (H²U) Consortium, Kampala, Uganda
- Makerere University College of Health Sciences, Kampala, Uganda
| | - Gregory D Kirk
- HIV and HCC in Uganda (H²U) Consortium, Kampala, Uganda
- Johns Hopkins University, Baltimore, MD, USA
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24
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Premi E, Pengo M, Mattioli I, Cantoni V, Dukart J, Gasparotti R, Buratti E, Padovani A, Bocchetta M, Todd EG, Bouzigues A, Cash DM, Convery RS, Russell LL, Foster P, Thomas DL, van Swieten JC, Jiskoot LC, Seelaar H, Galimberti D, Sanchez-Valle R, Laforce R, Moreno F, Synofzik M, Graff C, Masellis M, Tartaglia MC, Rowe JB, Tsvetanov KA, Vandenberghe R, Finger E, Tiraboschi P, de Mendonça A, Santana I, Butler CR, Ducharme S, Gerhard A, Levin J, Otto M, Sorbi S, Le Ber I, Pasquier F, Rohrer JD, Borroni B. Early neurotransmitters changes in prodromal frontotemporal dementia: A GENFI study. Neurobiol Dis 2023; 179:106068. [PMID: 36898614 DOI: 10.1016/j.nbd.2023.106068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 03/01/2023] [Accepted: 03/04/2023] [Indexed: 03/12/2023] Open
Abstract
BACKGROUND Neurotransmitters deficits in Frontotemporal Dementia (FTD) are still poorly understood. Better knowledge of neurotransmitters impairment, especially in prodromal disease stages, might tailor symptomatic treatment approaches. METHODS In the present study, we applied JuSpace toolbox, which allowed for cross-modal correlation of Magnetic Resonance Imaging (MRI)-based measures with nuclear imaging derived estimates covering various neurotransmitter systems including dopaminergic, serotonergic, noradrenergic, GABAergic and glutamatergic neurotransmission. We included 392 mutation carriers (157 GRN, 164 C9orf72, 71 MAPT), together with 276 non-carrier cognitively healthy controls (HC). We tested if the spatial patterns of grey matter volume (GMV) alterations in mutation carriers (relative to HC) are correlated with specific neurotransmitter systems in prodromal (CDR® plus NACC FTLD = 0.5) and in symptomatic (CDR® plus NACC FTLD≥1) FTD. RESULTS In prodromal stages of C9orf72 disease, voxel-based brain changes were significantly associated with spatial distribution of dopamine and acetylcholine pathways; in prodromal MAPT disease with dopamine and serotonin pathways, while in prodromal GRN disease no significant findings were reported (p < 0.05, Family Wise Error corrected). In symptomatic FTD, a widespread involvement of dopamine, serotonin, glutamate and acetylcholine pathways across all genetic subtypes was found. Social cognition scores, loss of empathy and poor response to emotional cues were found to correlate with the strength of GMV colocalization of dopamine and serotonin pathways (all p < 0.01). CONCLUSIONS This study, indirectly assessing neurotransmitter deficits in monogenic FTD, provides novel insight into disease mechanisms and might suggest potential therapeutic targets to counteract disease-related symptoms.
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Affiliation(s)
- Enrico Premi
- Neurology, Department of Neurological and Vision Sciences, ASST Spedali Civili, Brescia, Italy
| | - Marta Pengo
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy; Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Irene Mattioli
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Valentina Cantoni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research CentreJülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Roberto Gasparotti
- Neuroradiology Unit, Department of Medical and Surgical Specialties, University of Brescia, Brescia, Italy
| | | | - Alessandro Padovani
- Neurology, Department of Neurological and Vision Sciences, ASST Spedali Civili, Brescia, Italy; Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London, United Kingdom
| | - Emily G Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Arabella Bouzigues
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - David M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Rhian S Convery
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Lucy L Russell
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Phoebe Foster
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - David L Thomas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - John C van Swieten
- Department of Neurology and Alzheimer center, Erasmus Medical Center Rotterdam, the Netherlands
| | - Lize C Jiskoot
- Department of Neurology and Alzheimer center, Erasmus Medical Center Rotterdam, the Netherlands
| | - Harro Seelaar
- Department of Neurology and Alzheimer center, Erasmus Medical Center Rotterdam, the Netherlands
| | - Daniela Galimberti
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy; Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Raquel Sanchez-Valle
- Neurology Department, Hospital Clinic, Institut d'Investigacions Biomèdiques, Barcelona, Spain
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, Faculté de Médecine, Université Laval, Québec, Canada
| | - Fermin Moreno
- Hospital Universitario Donostia, San Sebastian, Spain
| | - Matthis Synofzik
- Division Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen, Tübingen, Germany; German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Caroline Graff
- Karolinska Institutet, Department NVS, Division of Neurogeriatrics, Stockholm, Sweden; Unit for Hereditray Dementia, Theme Aging, Karolinska University Hospital, Solna, Stockholm, Sweden
| | - Mario Masellis
- Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Maria Carmela Tartaglia
- Toronto Western Hospital, Tanz Centre for Research in Neurodegenerative Disease, Toronto, ON, Canada
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust and Medical Research Council Cognition and brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Kamen A Tsvetanov
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust and Medical Research Council Cognition and brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada
| | - Pietro Tiraboschi
- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Istituto Neurologico Carlo Besta, Milan, Italy
| | | | - Isabel Santana
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Portugal
| | - Chris R Butler
- Department of Clinical Neurology, University of Oxford, Oxford, United Kingdom
| | - Simon Ducharme
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Alexander Gerhard
- Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, United Kingdom; Departments of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Germany
| | - Johannes Levin
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster of System Neurology, Munich, Germany
| | - Markus Otto
- Department of Neurology, University Hospital Halle, Halle, Germany
| | - Sandro Sorbi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy; IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Isabelle Le Ber
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France; Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France; Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France; Reference Network for Rare Neurological Diseases (ERN-RND)
| | - Florence Pasquier
- University of Lille, France; Inserm 1172, Lille, France; CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, France
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Barbara Borroni
- Neurology, Department of Neurological and Vision Sciences, ASST Spedali Civili, Brescia, Italy; Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.
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25
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Coath W, Modat M, Cardoso MJ, Markiewicz PJ, Lane CA, Parker TD, Keshavan A, Buchanan SM, Keuss SE, Harris MJ, Burgos N, Dickson J, Barnes A, Thomas DL, Beasley D, Malone IB, Wong A, Erlandsson K, Thomas BA, Schöll M, Ourselin S, Richards M, Fox NC, Schott JM, Cash DM. Operationalizing the centiloid scale for [ 18F]florbetapir PET studies on PET/MRI. Alzheimers Dement (Amst) 2023; 15:e12434. [PMID: 37201176 PMCID: PMC10186069 DOI: 10.1002/dad2.12434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/03/2023] [Accepted: 02/19/2023] [Indexed: 05/20/2023]
Abstract
INTRODUCTION The Centiloid scale aims to harmonize amyloid beta (Aβ) positron emission tomography (PET) measures across different analysis methods. As Centiloids were created using PET/computerized tomography (CT) data and are influenced by scanner differences, we investigated the Centiloid transformation with data from Insight 46 acquired with PET/magnetic resonanceimaging (MRI). METHODS We transformed standardized uptake value ratios (SUVRs) from 432 florbetapir PET/MRI scans processed using whole cerebellum (WC) and white matter (WM) references, with and without partial volume correction. Gaussian-mixture-modelling-derived cutpoints for Aβ PET positivity were converted. RESULTS The Centiloid cutpoint was 14.2 for WC SUVRs. The relationship between WM and WC uptake differed between the calibration and testing datasets, producing implausibly low WM-based Centiloids. Linear adjustment produced a WM-based cutpoint of 18.1. DISCUSSION Transformation of PET/MRI florbetapir data to Centiloids is valid. However, further understanding of the effects of acquisition or biological factors on the transformation using a WM reference is needed. HIGHLIGHTS Centiloid conversion of amyloid beta positron emission tomography (PET) data aims to standardize results.Centiloid values can be influenced by differences in acquisition.We converted florbetapir PET/magnetic resonance imaging data from a large birth cohort.Whole cerebellum referenced values could be reliably transformed to Centiloids.White matter referenced values may be less generalizable between datasets.
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Affiliation(s)
- William Coath
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Marc Modat
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - M. Jorge Cardoso
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Pawel J. Markiewicz
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUCLLondonUK
| | | | - Thomas D. Parker
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Ashvini Keshavan
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Sarah M. Buchanan
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Sarah E. Keuss
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Matthew J. Harris
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Ninon Burgos
- Sorbonne Université, Institut du Cerveau ‐ Paris Brain Institute ‐ ICM, Inserm, CNRS, AP‐HP, Hôpital Pitié Salpêtrière, InriaAramis project‐teamParisFrance
| | - John Dickson
- Institute of Nuclear MedicineUniversity College London HospitalsLondonUK
| | - Anna Barnes
- Institute of Nuclear MedicineUniversity College London HospitalsLondonUK
| | - David L. Thomas
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
- Department of Brain Repair and RehabilitationUCL Queen Square Institute of NeurologyLondonUK
- Wellcome Centre for Human Neuroimaging, Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Daniel Beasley
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Ian B. Malone
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCLLondonUK
| | - Kjell Erlandsson
- Institute of Nuclear MedicineUniversity College London HospitalsLondonUK
| | - Benjamin A. Thomas
- Institute of Nuclear MedicineUniversity College London HospitalsLondonUK
| | - Michael Schöll
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska AcademyUniversity of GothenburgMölndalSweden
- Wallenberg Centre for Molecular and Translational MedicineUniversity of GothenburgMölndalSweden
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | | | - Nick C. Fox
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
- Dementia Research InstituteUCL Queen Square Institute of NeurologyLondonUK
| | | | - David M. Cash
- Dementia Research CentreUCL Queen Square Institute of NeurologyLondonUK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUCLLondonUK
- Dementia Research InstituteUCL Queen Square Institute of NeurologyLondonUK
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26
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Bocchetta M, Todd EG, Bouzigues A, Cash DM, Nicholas JM, Convery RS, Russell LL, Thomas DL, Malone IB, Iglesias JE, van Swieten JC, Jiskoot LC, Seelaar H, Borroni B, Galimberti D, Sanchez-Valle R, Laforce R, Moreno F, Synofzik M, Graff C, Masellis M, Tartaglia MC, Rowe JB, Vandenberghe R, Finger E, Tagliavini F, de Mendonça A, Santana I, Butler CR, Ducharme S, Gerhard A, Danek A, Levin J, Otto M, Sorbi S, Le Ber I, Pasquier F, Rohrer JD, Esteve AS, Nelson A, Heller C, Greaves CV, Benotmane H, Zetterberg H, Swift IJ, Samra K, Shafei R, Timberlake C, Cope T, Rittman T, Benussi A, Premi E, Gasparotti R, Archetti S, Gazzina S, Cantoni V, Arighi A, Fenoglio C, Scarpini E, Fumagalli G, Borracci V, Rossi G, Giaccone G, Di Fede G, Caroppo P, Tiraboschi P, Prioni S, Redaelli V, Tang-Wai D, Rogaeva E, Castelo-Branco M, Freedman M, Keren R, Black S, Mitchell S, Shoesmith C, Bartha R, Rademakers R, Poos J, Papma JM, Giannini L, van Minkelen R, Pijnenburg Y, Nacmias B, Ferrari C, Polito C, Lombardi G, Bessi V, Veldsman M, Andersson C, Thonberg H, Öijerstedt L, Jelic V, Thompson P, Langheinrich T, Lladó A, Antonell A, Olives J, Balasa M, Bargalló N, Borrego-Ecija S, Verdelho A, Maruta C, Ferreira CB, Miltenberger G, do Couto FS, Gabilondo A, Gorostidi A, Villanua J, Cañada M, Tainta M, Zulaica M, Barandiaran M, Alves P, Bender B, Wilke C, Graf L, Vogels A, Vandenbulcke M, Van Damme P, Bruffaerts R, Poesen K, Rosa-Neto P, Gauthier S, Camuzat A, Brice A, Bertrand A, Funkiewiez A, Rinaldi D, Saracino D, Colliot O, Sayah S, Prix C, Wlasich E, Wagemann O, Loosli S, Schönecker S, Hoegen T, Lombardi J, Anderl-Straub S, Rollin A, Kuchcinski G, Bertoux M, Lebouvier T, Deramecourt V, Santiago B, Duro D, Leitão MJ, Almeida MR, Tábuas-Pereira M, Afonso S. Structural MRI predicts clinical progression in presymptomatic genetic frontotemporal dementia: findings from the GENetic Frontotemporal dementia Initiative (GENFI) cohort. Brain Commun 2023; 5:fcad061. [PMID: 36970046 PMCID: PMC10036293 DOI: 10.1093/braincomms/fcad061] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/22/2022] [Accepted: 03/08/2023] [Indexed: 03/12/2023] Open
Abstract
Abstract
Biomarkers that can predict disease progression in individuals with genetic frontotemporal dementia are urgently needed. We aimed to identify whether baseline MRI-based grey and white matter abnormalities are associated with different clinical progression profiles in presymptomatic mutation carriers in the GENetic Frontotemporal dementia Initiative.
387 mutation carriers were included (160 GRN, 160 C9orf72, 67 MAPT), together with 240 non-carrier cognitively normal controls. Cortical and subcortical grey matter volumes were generated using automated parcellation methods on volumetric 3 T T1-weighted MRI scans, while white matter characteristics were estimated using diffusion tensor imaging. Mutation carriers were divided into two disease stages based on their global CDR®+NACC-FTLD score: presymptomatic (0 or 0.5) and fully symptomatic (1 or greater). W-scores in each grey matter volumes and white matter diffusion measures were computed to quantify the degree of abnormality compared to controls for each presymptomatic carrier, adjusting for their age, sex, total intracranial volume, and scanner type. Presymptomatic carriers were classified as “normal” or “abnormal” based on whether their grey matter volume and white matter diffusion measure w-scores were above or below the cut point corresponding to the 10th percentile of the controls. We then compared the change in disease severity between baseline and one year later in both the “normal” and “abnormal” groups within each genetic subtype, as measured by the CDR®+NACC-FTLD sum-of-boxes score and revised Cambridge Behavioural Inventory total score.
Overall, presymptomatic carriers with normal regional w-scores at baseline did not progress clinically as much as those with abnormal regional w-scores. Having abnormal grey or white matter measures at baseline was associated with a statistically significant increase in the CDR®+NACC-FTLD of up to 4 points in C9orf72 expansion carriers, and 5 points in the GRN group as well as a statistically significant increase in the revised Cambridge Behavioural Inventory of up to 11 points in MAPT, 10 points in GRN, and 8 points in C9orf72 mutation carriers.
Baseline regional brain abnormalities on MRI in presymptomatic mutation carriers are associated with different profiles of clinical progression over time. These results may be helpful to inform stratification of participants in future trials.
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Affiliation(s)
- Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London , United Kingdom
- Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London , London , United Kingdom
| | - Emily G Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London , United Kingdom
| | - Arabella Bouzigues
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London , United Kingdom
| | - David M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London , United Kingdom
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London , London , United Kingdom
| | - Jennifer M Nicholas
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine , London , United Kingdom
| | - Rhian S Convery
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London , United Kingdom
| | - Lucy L Russell
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London , United Kingdom
| | - David L Thomas
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London , United Kingdom
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London , London , United Kingdom
| | - Ian B Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London , United Kingdom
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London , London , United Kingdom
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School , Charlestown, MA , USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology , Cambridge, MA , USA
| | - John C van Swieten
- Department of Neurology and Alzheimer center, Erasmus Medical Center Rotterdam , Rotterdam , the Netherlands
| | - Lize C Jiskoot
- Department of Neurology and Alzheimer center, Erasmus Medical Center Rotterdam , Rotterdam , the Netherlands
| | - Harro Seelaar
- Department of Neurology and Alzheimer center, Erasmus Medical Center Rotterdam , Rotterdam , the Netherlands
| | - Barbara Borroni
- Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia , Brescia , Italy
| | - Daniela Galimberti
- Department of Biomedical, Surgical and Dental Sciences, University of Milan , Milan , Italy
- Fondazione IRCCS Ca’ Granda, Ospedale Maggiore Policlinico , Milan , Italy
| | - Raquel Sanchez-Valle
- Neurology Department, Hospital Clinic, Institut d’Investigacions Biomèdiques , Barcelona , Spain
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, Faculté de Médecine, Université Laval , Québec , Canada
| | - Fermin Moreno
- Hospital Universitario Donostia , San Sebastian , Spain
| | - Matthis Synofzik
- Division Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research (HIH), University of Tübingen , Tübingen , Germany
- German Center for Neurodegenerative Diseases (DZNE) , Tübingen , Germany
| | - Caroline Graff
- Karolinska Institutet, Department NVS, Division of Neurogeriatrics , Stockholm , Sweden
- Unit for Hereditary Dementia, Theme Aging, Karolinska University Hospital-Solna Stockholm , Stockholm , Sweden
| | - Mario Masellis
- Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute , Toronto, ON , Canada
| | - Maria Carmela Tartaglia
- Toronto Western Hospital, Tanz Centre for Research in Neurodegenerative Disease , Toronto, ON , Canada
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust and Medical Research Council Cognition and brain Sciences Unit, University of Cambridge , Cambridge , United Kingdom
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences , KU Leuven, Leuven , Belgium
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario , London, ON , Canada
| | - Fabrizio Tagliavini
- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico, Istituto Neurologico Carlo Besta , Milan , Italy
| | | | - Isabel Santana
- Neurology Department, Centro Hospitalar e Universitário de Coimbra , Coimbra , Portugal
| | - Chris R Butler
- Department of Clinical Neurology, University of Oxford , Oxford , United Kingdom
| | - Simon Ducharme
- Department of Neurology and Neurosurgery, McGill University , Montreal, Quebec , Canada
| | - Alexander Gerhard
- Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester , Manchester , United Kingdom
- Departments of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen , Essen , Germany
| | - Adrian Danek
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich; German Center for Neurodegenerative Diseases (DZNE) , Munich; Munich Cluster of Systems Neurology, Munich , Germany
| | - Johannes Levin
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich; German Center for Neurodegenerative Diseases (DZNE) , Munich; Munich Cluster of Systems Neurology, Munich , Germany
| | - Markus Otto
- Department of Neurology, University Hospital Ulm , Ulm , Germany
| | - Sandro Sorbi
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence , Florence , Italy
- IRCCS Fondazione Don Carlo Gnocchi , Florence , Italy
| | - Isabelle Le Ber
- Sorbonne Université, Paris Brain Institute – Institut du Cerveau– ICM , Inserm U1127, CNRS UMR 7225, AP-HP - Hôpital Pitié-Salpêtrière, Paris , France
- Centre deréférence des démences rares ou précoces , IM2A, Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris , France
- Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière , Paris , France
| | - Florence Pasquier
- Univ Lille , Lille , France
- Inserm 1172 , Lille , France
- CHU, CNR-MAJ, Labex Distalz, LiCENDLille , Lille , France
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London , London , United Kingdom
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Pisanic N, Antar AAR, Kruczynski KL, Gregory Rivera M, Dhakal S, Spicer K, Randad PR, Pekosz A, Klein SL, Betenbaugh MJ, Detrick B, Clarke W, Thomas DL, Manabe YC, Heaney CD. Methodological approaches to optimize multiplex oral fluid SARS-CoV-2 IgG assay performance and correlation with serologic and neutralizing antibody responses. J Immunol Methods 2023; 514:113440. [PMID: 36773929 PMCID: PMC9911157 DOI: 10.1016/j.jim.2023.113440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/25/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023]
Abstract
BACKGROUND Oral fluid (hereafter, saliva) is a non-invasive and attractive alternative to blood for SARS-CoV-2 IgG testing; however, the heterogeneity of saliva as a matrix poses challenges for immunoassay performance. OBJECTIVES To optimize performance of a magnetic microparticle-based multiplex immunoassay (MIA) for SARS-CoV-2 IgG measurement in saliva, with consideration of: i) threshold setting and validation across different MIA bead batches; ii) sample qualification based on salivary total IgG concentration; iii) calibration to U.S. SARS-CoV-2 serological standard binding antibody units (BAU); and iv) correlations with blood-based SARS-CoV-2 serological and neutralizing antibody (nAb) assays. METHODS The salivary SARS-CoV-2 IgG MIA included 2 nucleocapsid (N), 3 receptor-binding domain (RBD), and 2 spike protein (S) antigens. Gingival crevicular fluid (GCF) swab saliva samples were collected before December 2019 (n = 555) and after molecular test-confirmed SARS-CoV-2 infection from 113 individuals (providing up to 5 repeated-measures; n = 398) and used to optimize and validate MIA performance (total n = 953). Combinations of IgG responses to N, RBD and S and total salivary IgG concentration (μg/mL) as a qualifier of nonreactive samples were optimized and validated, calibrated to the U.S. SARS-CoV-2 serological standard, and correlated with blood-based SARS-CoV-2 IgG ELISA and nAb assays. RESULTS The sum of signal to cutoff (S/Co) to all seven MIA SARS-CoV-2 antigens and disqualification of nonreactive saliva samples with ≤15 μg/mL total IgG led to correct classification of 62/62 positives (sensitivity [Se] = 100.0%; 95% confidence interval [CI] = 94.8%, 100.0%) and 108/109 negatives (specificity [Sp] = 99.1%; 95% CI = 97.3%, 100.0%) at 8-million beads coupling scale and 80/81 positives (Se = 98.8%; 95% CI = 93.3%, 100.0%] and 127/127 negatives (Sp = 100%; 95% CI = 97.1%, 100.0%) at 20-million beads coupling scale. Salivary SARS-CoV-2 IgG crossed the MIA cutoff of 0.1 BAU/mL on average 9 days post-COVID-19 symptom onset and peaked around day 30. Among n = 30 matched saliva and plasma samples, salivary SARS-CoV-2 MIA IgG levels correlated with corresponding-antigen plasma ELISA IgG (N: ρ = 0.76, RBD: ρ = 0.83, S: ρ = 0.82; all p < 0.001). Correlations of plasma SARS-CoV-2 nAb assay area under the curve (AUC) with salivary MIA IgG (N: ρ = 0.68, RBD: ρ = 0.78, S: ρ = 0.79; all p < 0.001) and with plasma ELISA IgG (N: ρ = 0.76, RBD: ρ = 0.79, S: ρ = 0.76; p < 0.001) were similar. CONCLUSIONS A salivary SARS-CoV-2 IgG MIA produced consistently high Se (> 98.8%) and Sp (> 99.1%) across two bead coupling scales and correlations with nAb responses that were similar to blood-based SARS-CoV-2 IgG ELISA data. This non-invasive salivary SARS-CoV-2 IgG MIA could increase engagement of vulnerable populations and improve broad understanding of humoral immunity (kinetics and gaps) within the evolving context of booster vaccination, viral variants and waning immunity.
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Affiliation(s)
- Nora Pisanic
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
| | - Annukka A R Antar
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Kate L Kruczynski
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Magdielis Gregory Rivera
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Santosh Dhakal
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Kristoffer Spicer
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Pranay R Randad
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Andrew Pekosz
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Sabra L Klein
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of Biochemistry and Molecular Biology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Barbara Detrick
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - William Clarke
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David L Thomas
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Yukari C Manabe
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Christopher D Heaney
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
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28
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Foda ZH, Annapragada AV, Boyapati K, Bruhm DC, Vulpescu NA, Medina JE, Mathios D, Cristiano S, Niknafs N, Luu HT, Goggins MG, Anders RA, Sun J, Meta SH, Thomas DL, Kirk GD, Adleff V, Phallen J, Scharpf RB, Kim AK, Velculescu VE. Detecting Liver Cancer Using Cell-Free DNA Fragmentomes. Cancer Discov 2023; 13:616-631. [PMID: 36399356 PMCID: PMC9975663 DOI: 10.1158/2159-8290.cd-22-0659] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/08/2022] [Accepted: 11/17/2022] [Indexed: 11/21/2022]
Abstract
Liver cancer is a major cause of cancer mortality worldwide. Screening individuals at high risk, including those with cirrhosis and viral hepatitis, provides an avenue for improved survival, but current screening methods are inadequate. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome analyses to evaluate 724 individuals from the United States, the European Union, or Hong Kong with hepatocellular carcinoma (HCC) or who were at average or high-risk for HCC. Using a machine learning model that incorporated multifeature fragmentome data, the sensitivity for detecting cancer was 88% in an average-risk population at 98% specificity and 85% among high-risk individuals at 80% specificity. We validated these results in an independent population. cfDNA fragmentation changes reflected genomic and chromatin changes in liver cancer, including from transcription factor binding sites. These findings provide a biological basis for changes in cfDNA fragmentation in patients with liver cancer and provide an accessible approach for noninvasive cancer detection. SIGNIFICANCE There is a great need for accessible and sensitive screening approaches for HCC worldwide. We have developed an approach for examining genome-wide cfDNA fragmentation features to provide a high-performing and cost-effective approach for liver cancer detection. See related commentary Rolfo and Russo, p. 532. This article is highlighted in the In This Issue feature, p. 517.
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Affiliation(s)
- Zachariah H. Foda
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Akshaya V. Annapragada
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Kavya Boyapati
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Daniel C. Bruhm
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Nicholas A. Vulpescu
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jamie E. Medina
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Dimitrios Mathios
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Stephen Cristiano
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Noushin Niknafs
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Harry T. Luu
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Michael G. Goggins
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Robert A. Anders
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jing Sun
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Shruti H. Meta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - David L. Thomas
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Gregory D. Kirk
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Vilmos Adleff
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jillian Phallen
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Robert B. Scharpf
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Amy K. Kim
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Victor E. Velculescu
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
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29
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Green RE, Lord J, Scelsi MA, Xu J, Wong A, Naomi-James S, Handy A, Gilchrist L, Williams DM, Parker TD, Lane CA, Malone IB, Cash DM, Sudre CH, Coath W, Thomas DL, Keuss S, Dobson R, Legido-Quigley C, Fox NC, Schott JM, Richards M, Proitsi P. Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer's disease. Alzheimers Res Ther 2023; 15:38. [PMID: 36814324 PMCID: PMC9945600 DOI: 10.1186/s13195-023-01184-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 02/08/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46-the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer's disease (AD). METHODS Following quality control, levels of 1019 metabolites-detected with liquid chromatography-mass spectrometry-were available for 1740 participants at age 60-64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69-71). Regression analyses tested relationships between metabolite measures-modules and hub metabolites-and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (pFDR < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (pFDR < 0.05) with an imaging outcome (N = 1638). RESULTS In the fully adjusted model, three lipid modules were associated with a brain volume measure (pFDR < 0.05): one enriched in sphingolipids (hippocampal volume: ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß = - 0.072, 95%CI = [- 0.12, - 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß = - 0.066, 95% CI = [- 0.11, - 0.020]). Twenty-two hub metabolites were associated (pFDR < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction. CONCLUSIONS Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.
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Affiliation(s)
- Rebecca E Green
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK
| | - Jodie Lord
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Marzia A Scelsi
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Jin Xu
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,Institute of Pharmaceutical Science, King's College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK
| | - Sarah Naomi-James
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Alex Handy
- University College London, Institute of Health Informatics, London, UK
| | - Lachlan Gilchrist
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Dylan M Williams
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,Department of Brain Sciences, Imperial College London, London, W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London, W12 0BZ, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Carole H Sudre
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK.,MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - David L Thomas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Richard Dobson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK.,University College London, Institute of Health Informatics, London, UK.,Health Data Research UK London, University College London, London, UK.,NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King's College London, London, UK.,Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
| | - Marcus Richards
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.
| | - Petroula Proitsi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.
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30
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Shoham S, Bloch EM, Casadevall A, Hanley D, Lau B, Gebo K, Cachay E, Kassaye SG, Paxton JH, Gerber J, Levine AC, Naeim A, Currier J, Patel B, Allen ES, Anjan S, Appel L, Baksh S, Blair PW, Bowen A, Broderick P, Caputo CA, Cluzet V, Elena MC, Cruser D, Ehrhardt S, Forthal D, Fukuta Y, Gawad AL, Gniadek T, Hammel J, Huaman MA, Jabs DA, Jedlicka A, Karlen N, Klein S, Laeyendecker O, Karen L, McBee N, Meisenberg B, Merlo C, Mosnaim G, Park HS, Pekosz A, Petrini J, Rausch W, Shade DM, Shapiro JR, Singleton RJ, Sutcliffe C, Thomas DL, Yarava A, Zand M, Zenilman JM, Tobian AA, Sullivan DJ. Transfusing Convalescent Plasma as Post-Exposure Prophylaxis Against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection: A Double-Blinded, Phase 2 Randomized, Controlled Trial. Clin Infect Dis 2023; 76:e477-e486. [PMID: 35579509 PMCID: PMC9129191 DOI: 10.1093/cid/ciac372] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 04/18/2022] [Accepted: 05/10/2022] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The efficacy of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) convalescent plasma (CCP) for preventing infection in exposed, uninfected individuals is unknown. CCP might prevent infection when administered before symptoms or laboratory evidence of infection. METHODS This double-blinded, phase 2 randomized, controlled trial (RCT) compared the efficacy and safety of prophylactic high titer (≥1:320 by Euroimmun ELISA) CCP with standard plasma. Asymptomatic participants aged ≥18 years with close contact exposure to a person with confirmed coronavirus disease 2019 (COVID-19) in the previous 120 hours and negative SARS-CoV-2 test within 24 hours before transfusion were eligible. The primary outcome was new SARS-CoV-2 infection. RESULTS In total, 180 participants were enrolled; 87 were assigned to CCP and 93 to control plasma, and 170 transfused at 19 sites across the United States from June 2020 to March 2021. Two were excluded for screening SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) positivity. Of the remaining 168 participants, 12/81 (14.8%) CCP and 13/87 (14.9%) control recipients developed SARS-CoV-2 infection; 6 (7.4%) CCP and 7 (8%) control recipients developed COVID-19 (infection with symptoms). There were no COVID-19-related hospitalizations in CCP and 2 in control recipients. Efficacy by restricted mean infection free time (RMIFT) by 28 days for all SARS-CoV-2 infections (25.3 vs 25.2 days; P = .49) and COVID-19 (26.3 vs 25.9 days; P = .35) was similar for both groups. CONCLUSIONS Administration of high-titer CCP as post-exposure prophylaxis, although appearing safe, did not prevent SARS-CoV-2 infection. CLINICAL TRIALS REGISTRATION NCT04323800.
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Affiliation(s)
| | | | | | | | - Bryan Lau
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA, Mosaic Consulting Ltd., Israel
| | | | - Edward Cachay
- Department of Medicine, Division of Infectious Diseases
| | - Seble G. Kassaye
- Division of Infectious Diseases/Department of Medicine, Georgetown University Medical Center, Washington, DC, USA
| | - James H. Paxton
- Department of Emergency Medicine Wayne State University, Detroit, Michigan, USA
| | - Jonathan Gerber
- Department of Medicine, Division of Hematology and Oncology, University of Massachusetts Chan Medical School, Worchester, Massachusetts, USA
| | - Adam C Levine
- Department of Emergency Medicine, Rhode Island Hospital/Brown University, Providence, Rhode Island, USA
| | - Arash Naeim
- Department of Medicine, Division of Infectious Diseases, University of California, Los Angeles, Los Angeles, California, USA
| | - Judith Currier
- Department of Medicine, Division of Infectious Diseases, University of California, Los Angeles, Los Angeles, California, USA
| | - Bela Patel
- Department of Medicine, Division Critical Care Medicine, University of Texas Health, Houston, Texas, USA
| | - Elizabeth S. Allen
- Department of Pathology, University of California, San Diego, San Diego, California, USA
| | - Shweta Anjan
- Department of Medicine, Division of Infectious Diseases, University of Miami Miller School of Medicine, Miami, Florida, USA
| | | | - Sheriza Baksh
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA, Mosaic Consulting Ltd., Israel
| | | | | | | | | | - Valerie Cluzet
- Vassar Brothers Medical Center, Nuvance Health, Poughkeepsie, New York, USA
| | | | | | - Stephan Ehrhardt
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA, Mosaic Consulting Ltd., Israel
| | - Donald Forthal
- Department of Medicine, Division of Infectious Diseases, University of California, Irvine, Irvine, California, USA
| | - Yuriko Fukuta
- Department of Medicine, Section of Infectious Diseases, Baylor College of Medicine, Houston, Texas, USA
| | | | - Thomas Gniadek
- Department of Pathology, Northshore University Health System, Evanston, Illinois, USA
| | | | - Moises A. Huaman
- Department of Medicine, Division of Infectious Diseases, University of Cincinnati, Cincinnati, Ohio, USA
| | - Douglas A. Jabs
- Department of Ophthalmology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | | | - Sabra Klein
- Department of Molecular Microbiology and Immunology
| | - Oliver Laeyendecker
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Baltimore, Maryland, USA
| | | | | | | | | | | | - Han-Sol Park
- Department of Molecular Microbiology and Immunology
| | | | - Joann Petrini
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Baltimore, Maryland, USA
| | - William Rausch
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Baltimore, Maryland, USA
| | - David M. Shade
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA, Mosaic Consulting Ltd., Israel
| | | | | | - Catherine Sutcliffe
- Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA, Mosaic Consulting Ltd., Israel
| | | | | | - Martin Zand
- Department of Medicine, University of Rochester, Rochester, New York, USA
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Duchen D, Vergara C, Thio CL, Kundu P, Chatterjee N, Thomas DL, Wojcik GL, Duggal P. Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects. Am J Hum Genet 2023; 110:336-348. [PMID: 36649706 PMCID: PMC9943744 DOI: 10.1016/j.ajhg.2022.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 12/20/2022] [Indexed: 01/18/2023] Open
Abstract
Genome-wide association studies (GWASs) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common control subjects from biobanks and extensive consortia is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the control subjects are not well characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of affected subjects to population-based common control subjects regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen-exposed cases and population-based common control subjects, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well-characterized control subjects and population-based common control subjects from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance associations. These findings suggest that the choice of control subjects is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.
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Affiliation(s)
- Dylan Duchen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Candelaria Vergara
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Chloe L Thio
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Prosenjit Kundu
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - David L Thomas
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Priya Duggal
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
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Hyde Z, Roura R, Signer D, Patel A, Cohen J, Saheed M, Brinkley S, Irvin R, Sulkowski MS, Thomas DL, Rothman RE, Hsieh YH. Evaluation of a pilot emergency department linkage to care program for patients previously diagnosed with Hepatitis C. J Viral Hepat 2023; 30:129-137. [PMID: 36441638 PMCID: PMC9852079 DOI: 10.1111/jvh.13774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 11/11/2022] [Accepted: 11/16/2022] [Indexed: 11/29/2022]
Abstract
There is a significant number of Emergency Department (ED) patients with known chronic hepatitis C virus (HCV) infection who have not been treated with directly acting antivirals. We implemented a pilot ED-based linkage-to-care program to address this need and evaluated the impact of the program using the HCV Care Continuum metrics. Between March 2015 and May 2016, dedicated patient care navigators identified HCV RNA-positive patients in an urban ED and offered expedited appointments with the on-site viral hepatitis clinic. Patient demographics and care continuum outcomes were abstracted from the EMR and analysed to determine significant factors influencing linkage-to-care (LTC) and treatment initiation rates. The ED linkage-to-care program achieved a 43% linkage-to-care rate (165/384), 22% treatment rate (84/384) and 16% sustained virologic response rate (63/384). Significant associations were found between linkage-to-care and increasing age (OR = 1.03), Medicare insurance (OR = 2.21) and having a primary care physician (PCP) (OR = 4.03). For patients who were linked, the odds of initiating treatment were also positively significantly associated with increasing age (OR = 1.04) and having a PCP (OR = 2.77). For patients who initiated treatment, the odds of sustained virologic response were marginally associated with having a PCP (OR = 4.92).Our ED linkage-to-care program utilized care coordination to successfully link nearly half of approached HCV RNA-positive patients to care. This design can be feasibly replicated by other EDs given limited non-clinical training required for linkage-to-care staff. Adoption of similar programs in other EDs may improve the rates of LTC and treatment initiation for previously diagnosed HCV patients.
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Affiliation(s)
- Zak Hyde
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Raúl Roura
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Danielle Signer
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anuj Patel
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jacob Cohen
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mustapha Saheed
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sherilyn Brinkley
- Division Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Risha Irvin
- Division Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mark S. Sulkowski
- Division Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David L. Thomas
- Division Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Richard E. Rothman
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Division Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Yu-Hsiang Hsieh
- Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Antar AAR, Yu T, Demko ZO, Hu C, Tornheim JA, Blair PW, Thomas DL, Manabe YC. Long COVID brain fog and muscle pain are associated with longer time to clearance of SARS-CoV-2 RNA from the upper respiratory tract during acute infection. medRxiv 2023:2023.01.18.23284742. [PMID: 36711478 PMCID: PMC9882625 DOI: 10.1101/2023.01.18.23284742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The incidence of long COVID is substantial, even in people who did not require hospitalization for acute COVID-19. The pathobiological mechanisms of long COVID and the role of early viral kinetics in its development are largely unknown. Seventy-three non-hospitalized adult participants were enrolled within approximately 48 hours of their first positive SARS-CoV-2 RT-PCR test, and mid-turbinate nasal and saliva samples were collected up to 9 times within the first 45 days after enrollment. Samples were assayed for SARS-CoV-2 using RT-PCR and additional test results were abstracted from the clinical record. Each participant indicated the presence and severity of 49 long- COVID symptoms at 1-, 3-, 6-, 12-, and 18-months post-COVID-19 diagnosis. Time from acute COVID-19 illness onset to SARS-CoV-2 RNA clearance greater or less than 28 days was tested for association with the presence or absence of each of 49 long COVID symptoms at 90+ days from acute COVID-19 symptom onset. Brain fog and muscle pain at 90+ days after acute COVID-19 onset were negatively associated with viral RNA clearance within 28 days of acute COVID-19 onset with adjustment for age, sex, BMI ≥ 25, and COVID vaccination status prior to COVID-19 (brain fog: aRR 0.46, 95% CI 0.22-0.95; muscle pain: aRR 0.28, 95% CI 0.08-0.94). This work indicates that at least two long COVID symptoms - brain fog and muscle pain - at 90+ days from acute COVID-19 onset are specifically associated with longer time to clearance of SARS-CoV-2 RNA from the upper respiratory tract.
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Duchen D, Clipman S, Vergara C, Thio CL, Thomas DL, Duggal P, Wojcik GL. A hepatitis B virus (HBV) sequence variation graph improves sequence alignment and sample-specific consensus sequence construction for genetic analysis of HBV. bioRxiv 2023:2023.01.11.523611. [PMID: 36711598 PMCID: PMC9882026 DOI: 10.1101/2023.01.11.523611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Hepatitis B virus (HBV) remains a global public health concern, with over 250 million individuals living with chronic HBV infection (CHB) and no curative therapy currently available. Viral diversity is associated with CHB pathogenesis and immunological control of infection. Improved methods to characterize the viral genome at both the population and intra-host level could aid drug development efforts. Conventionally, HBV sequencing data are aligned to a linear reference genome and only sequences capable of aligning to the reference are captured for analysis. Reference selection has additional consequences, including sample-specific 'consensus' sequence construction. It remains unclear how to select a reference from available sequences and whether a single reference is sufficient for genetic analyses. Using simulated short-read sequencing data generated from full-length publicly available HBV genome sequences and HBV sequencing data from a longitudinally sampled individual with CHB, we investigate alternative graph-based alignment approaches. We demonstrate that using a phylogenetically representative 'genome graph' for alignment, rather than linear reference sequences, avoids issues of reference ambiguity, improves alignment, and facilitates the construction of sample-specific consensus sequences genetically similar to an individual's infection. Graph-based methods can therefore improve efforts to characterize the genetics of viral pathogens, including HBV, and may have broad implications in host pathogen research.
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Affiliation(s)
- Dylan Duchen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Steven Clipman
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Candelaria Vergara
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Chloe L Thio
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - David L Thomas
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Priya Duggal
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
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Luke RA, Kearsley AJ, Pisanic N, Manabe YC, Thomas DL, Heaney CD, Patrone PN. Modeling in higher dimensions to improve diagnostic testing accuracy: Theory and examples for multiplex saliva-based SARS-CoV-2 antibody assays. PLoS One 2023; 18:e0280823. [PMID: 36913381 PMCID: PMC10010503 DOI: 10.1371/journal.pone.0280823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/05/2023] [Indexed: 03/14/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has emphasized the importance and challenges of correctly interpreting antibody test results. Identification of positive and negative samples requires a classification strategy with low error rates, which is hard to achieve when the corresponding measurement values overlap. Additional uncertainty arises when classification schemes fail to account for complicated structure in data. We address these problems through a mathematical framework that combines high dimensional data modeling and optimal decision theory. Specifically, we show that appropriately increasing the dimension of data better separates positive and negative populations and reveals nuanced structure that can be described in terms of mathematical models. We combine these models with optimal decision theory to yield a classification scheme that better separates positive and negative samples relative to traditional methods such as confidence intervals (CIs) and receiver operating characteristics. We validate the usefulness of this approach in the context of a multiplex salivary SARS-CoV-2 immunoglobulin G assay dataset. This example illustrates how our analysis: (i) improves the assay accuracy, (e.g. lowers classification errors by up to 42% compared to CI methods); (ii) reduces the number of indeterminate samples when an inconclusive class is permissible, (e.g. by 40% compared to the original analysis of the example multiplex dataset) and (iii) decreases the number of antigens needed to classify samples. Our work showcases the power of mathematical modeling in diagnostic classification and highlights a method that can be adopted broadly in public health and clinical settings.
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Affiliation(s)
- Rayanne A. Luke
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, United States of America
- Applied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, MD, United States of America
- * E-mail:
| | - Anthony J. Kearsley
- Applied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Nora Pisanic
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, United States of America
| | - Yukari C. Manabe
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States of America
| | - David L. Thomas
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States of America
| | - Christopher D. Heaney
- Department of Environmental Health and Engineering, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Internal Health, Johns Hopkins University, Baltimore, MD, United States of America
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, United States of America
| | - Paul N. Patrone
- Applied and Computational Mathematics Division, National Institute of Standards and Technology, Gaithersburg, MD, United States of America
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Wennerstrom A, Manogue S, Hardeo H, Robinson WT, Thomas DL, Irvin R. Incarceration, Inequality, and Hepatitis C Treatment: The Story of Two Southern States. J Health Care Poor Underserved 2023; 34:1129-1135. [PMID: 38015141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Hepatitis C virus (HCV) infection causes liver-related morbidity/mortality and disproportionately affects people who are incarcerated and non-Hispanic Black populations, largely due to social and policy issues that contribute to poor health. With the advent of highly efficacious treatment, HCV is now curable. However, most states' departments of corrections do not offer universal HCV testing or treatment. Two southern states-Tennessee and Louisiana-provide examples of divergent approaches to addressing HCV infection. While Tennessee has offered treatment on a limited basis, resulting in a class action lawsuit, the state of Louisiana recently adopted a new approach. In establishing the 2019 Hepatitis Elimination Plan, the state created a standard of care for HCV infection that included robust testing and treatment in state prison facilities while capping costs. Louisiana has demonstrated the feasibility of HCV testing and treatment programs within state prisons, an important step towards achieving health equity.
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Alsulami TA, Hyare H, Thomas DL, Golay X. The value of arterial spin labelling (ASL) perfusion MRI in the assessment of post-treatment progression in adult glioma: A systematic review and meta-analysis. Neurooncol Adv 2023; 5:vdad122. [PMID: 37841694 PMCID: PMC10576519 DOI: 10.1093/noajnl/vdad122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023] Open
Abstract
Background The distinction between viable tumor and therapy-induced changes is crucial for the clinical management of patients with gliomas. This study aims to quantitatively assess the efficacy of arterial spin labeling (ASL) biomarkers, including relative cerebral blood flow (rCBF) and absolute cerebral blood flow (CBF), for the discrimination of progressive disease (PD) and treatment-related effects. Methods Eight articles were included in the synthesis after searching the literature systematically. Data have been extracted and a meta-analysis using the random-effect model was subsequently carried out. Diagnostic accuracy assessment was also performed. Results This study revealed that there is a significant difference in perfusion measurements between groups with PD and therapy-induced changes. The rCBF yielded a standardized mean difference (SMD) of 1.25 [95% CI 0.75, 1.75] (p < .00001). The maximum perfusion indices (rCBFmax and CBFmax) both showed equivalent discriminatory ability, with SMD of 1.35 [95% CI 0.78, 1.91] (p < .00001) and 1.56 [95% CI 0.79, 2.33] (p < .0001), respectively. Similarly, accuracy estimates were comparable among ASL-derived metrices. Pooled sensitivities [95% CI] were 0.85 [0.67, 0.94], 0.88 [0.71, 0.96], and 0.93 [0.73, 0.98], and pooled specificities [95% CI] were 0.83 [0.71, 0.91], 0.83 [0.67, 0.92], 0.84 [0.67, 0.93], for rCBF, rCBFmax and CBFmax, respectively. Corresponding HSROC area under curve (AUC) [95% CI] were 0.90 [0.87, 0.92], 0.92 [0.89, 0.94], and 0.93 [0.90, 0.95]. Conclusion These results suggest that ASL quantitative biomarkers, particularly rCBFmax and CBFmax, have the potential to discriminate between glioma progression and therapy-induced changes.
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Affiliation(s)
- Tamadur A Alsulami
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, King Abdulaziz University (KAU), Jeddah, Saudi Arabia
| | - Harpreet Hyare
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- University College London Hospitals NHS Foundation Trust, London, UK
| | - David L Thomas
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Xavier Golay
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Lysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College Hospitals NHS Trust, London, UK
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Antar AAR, Yu T, Demko ZO, Hu C, Tornheim JA, Blair PW, Thomas DL, Manabe YC. Long COVID brain fog and muscle pain are associated with longer time to clearance of SARS-CoV-2 RNA from the upper respiratory tract during acute infection. Front Immunol 2023; 14:1147549. [PMID: 37187756 PMCID: PMC10176965 DOI: 10.3389/fimmu.2023.1147549] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/13/2023] [Indexed: 05/17/2023] Open
Abstract
Introduction The incidence of long COVID is substantial, even in people with mild to moderate acute COVID-19. The role of early viral kinetics in the subsequent development of long COVID is largely unknown, especially in individuals who were not hospitalized for acute COVID-19. Methods Seventy-three non-hospitalized adult participants were enrolled within approximately 48 hours of their first positive SARS-CoV-2 RT-PCR test, and mid-turbinate nasal and saliva samples were collected up to 9 times within the first 45 days after enrollment. Samples were assayed for SARS-CoV-2 using RT-PCR and additional SARS-CoV-2 test results were abstracted from the clinical record. Each participant indicated the presence and severity of 49 long COVID symptoms at 1-, 3-, 6-, 12-, and 18-months post-COVID-19 diagnosis. Time from acute COVID-19 illness onset to SARS-CoV-2 RNA clearance greater or less than 28 days was tested for association with the presence or absence of each of 49 long COVID symptoms at 90+ days from acute COVID-19 symptom onset. Results Self-reported brain fog and muscle pain at 90+ days after acute COVID-19 onset were negatively associated with viral RNA clearance within 28 days of acute COVID-19 onset with adjustment for age, sex, BMI ≥ 25, and COVID vaccination status prior to COVID-19 (brain fog: aRR 0.46, 95% CI 0.22-0.95; muscle pain: aRR 0.28, 95% CI 0.08-0.94). Participants reporting higher severity brain fog or muscle pain at 90+ days after acute COVID-19 onset were less likely to have cleared SARS-CoV-2 RNA within 28 days. The acute viral RNA decay trajectories of participants who did and did not later go on to experience brain fog 90+ days after acute COVID-19 onset were distinct. Discussion This work indicates that at least two long COVID symptoms - brain fog and muscle pain - at 90+ days from acute COVID-19 onset are specifically associated with prolonged time to clearance of SARS-CoV-2 RNA from the upper respiratory tract during acute COVID-19. This finding provides evidence that delayed immune clearance of SARS-CoV-2 antigen or greater amount or duration of viral antigen burden in the upper respiratory tract during acute COVID-19 are directly linked to long COVID. This work suggests that host-pathogen interactions during the first few weeks after acute COVID-19 onset have an impact on long COVID risk months later.
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Affiliation(s)
- Annukka A. R. Antar
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- *Correspondence: Annukka A. R. Antar,
| | - Tong Yu
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Zoe O Demko
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Chen Hu
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Jeffrey A. Tornheim
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Paul W. Blair
- Austere environments Consortium for Enhanced Sepsis Outcomes (ACESO), Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, United States
| | - David L. Thomas
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Yukari C. Manabe
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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Pisanic N, Antar AAR, Kruczynski K, Rivera MG, Dhakal S, Spicer K, Randad PR, Pekosz A, Klein SL, Betenbaugh MJ, Detrick B, Clarke W, Thomas DL, Manabe YC, Heaney CD. Methodological approaches to optimize multiplex oral fluid SARS-CoV-2 IgG assay performance and correlation with serologic and neutralizing antibody responses. medRxiv 2022:2022.12.22.22283858. [PMID: 36597525 PMCID: PMC9810233 DOI: 10.1101/2022.12.22.22283858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Background Oral fluid (hereafter, saliva) is a non-invasive and attractive alternative to blood for SARS-CoV-2 IgG testing; however, the heterogeneity of saliva as a matrix poses challenges for immunoassay performance. Objectives To optimize performance of a magnetic microparticle-based multiplex immunoassay (MIA) for SARS-CoV-2 IgG measurement in saliva, with consideration of: i) threshold setting and validation across different MIA bead batches; ii) sample qualification based on salivary total IgG concentration; iii) calibration to U.S. SARS-CoV-2 serological standard binding antibody units (BAU); and iv) correlations with blood-based SARS-CoV-2 serological and neutralizing antibody (nAb) assays. Methods The salivary SARS-CoV-2 IgG MIA included 2 nucleocapsid (N), 3 receptor-binding domain (RBD), and 2 spike protein (S) antigens. Gingival crevicular fluid (GCF) swab saliva samples were collected before December, 2019 (n=555) and after molecular test-confirmed SARS-CoV-2 infection from 113 individuals (providing up to 5 repeated-measures; n=398) and used to optimize and validate MIA performance (total n=953). Combinations of IgG responses to N, RBD and S and total salivary IgG concentration (μg/mL) as a qualifier of nonreactive samples were optimized and validated, calibrated to the U.S. SARS-CoV-2 serological standard, and correlated with blood-based SARS-CoV-2 IgG ELISA and nAb assays. Results The sum of signal to cutoff (S/Co) to all seven MIA SARS-CoV-2 antigens and disqualification of nonreactive saliva samples with ≤15 μg/mL total IgG led to correct classification of 62/62 positives (sensitivity [Se]=100.0%; 95% confidence interval [CI]=94.8%, 100.0%) and 108/109 negatives (specificity [Sp]=99.1%; 95% CI=97.3%, 100.0%) at 8-million beads coupling scale and 80/81 positives (Se=98.8%; 95% CI=93.3%, 100.0%] and 127/127 negatives (Sp=100%; 95% CI=97.1%, 100.0%) at 20-million beads coupling scale. Salivary SARS-CoV-2 IgG crossed the MIA cutoff of 0.1 BAU/mL on average 9 days post-COVID-19 symptom onset and peaked around day 30. Among n=30 matched saliva and plasma samples, salivary SARS-CoV-2 MIA IgG levels correlated with corresponding-antigen plasma ELISA IgG (N: ρ=0.67, RBD: ρ=0.76, S: ρ=0.82; all p <0.0001). Correlations of plasma SARS-CoV-2 nAb assay area under the curve (AUC) with salivary MIA IgG (N: ρ=0.68, RBD: ρ=0.78, S: ρ=0.79; all p <0.0001) and with plasma ELISA IgG (N: ρ=0.76, RBD: ρ=0.79, S: ρ=0.76; p <0.0001) were similar. Conclusions A salivary SARS-CoV-2 IgG MIA produced consistently high Se (>98.8%) and Sp (>99.1%) across two bead coupling scales and correlations with nAb responses that were similar to blood-based SARS-CoV-2 IgG ELISA data. This non-invasive salivary SARS-CoV-2 IgG MIA could increase engagement of vulnerable populations and improve broad understanding of humoral immunity (kinetics and gaps) within the evolving context of booster vaccination, viral variants and waning immunity.
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Affiliation(s)
- Nora Pisanic
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Annukka A. R. Antar
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kate Kruczynski
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Magdielis Gregory Rivera
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Santosh Dhakal
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Kristoffer Spicer
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Pranay R. Randad
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Andrew Pekosz
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sabra L. Klein
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Biochemistry and Molecular Biology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Michael J. Betenbaugh
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Barbara Detrick
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - William Clarke
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - David L. Thomas
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yukari C. Manabe
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Christopher D. Heaney
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
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40
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Keuss SE, Cash DM, Nicholas JM, Parker TD, Lane CA, Keshavan A, Buchanan SM, Wagen AZ, Storey M, Harris MJ, Lu K, James S, Street RE, Barnes J, Malone IB, Sudre CH, Thomas DL, Dickson J, Murray‐Smith H, Freiberger T, Wong A, Crutch SJ, Richards M, Fox NC, Schott JM, Coath W. Rates of cortical thinning in Alzheimer’s disease signature regions: pathological influences and cognitive consequences in members of the 1946 British birth cohort. Alzheimers Dement 2022. [DOI: 10.1002/alz.067336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Sarah E Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - David M Cash
- Institute of Neurology, University College London London United Kingdom
| | - Jennifer M Nicholas
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine London United Kingdom
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
- UK DRI Centre for Care Research and Technology, Imperial College London London United Kingdom
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Ashvini Keshavan
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Sarah M Buchanan
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Aaron Z Wagen
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Mathew Storey
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Matthew J Harris
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Kirsty Lu
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Sarah‐Naomi James
- MRC Unit for Lifelong Health and Ageing at UCL London United Kingdom
| | - Rebecca E Street
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Jo Barnes
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Carole H Sudre
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
- School of Biomedical Engineering and Imaging Sciences, King’s College London London United Kingdom
- Centre for Medical Image Computing, University College London London United Kingdom
- MRC Unit for Lifelong Health and Ageing at UCL, University College London London United Kingdom
| | - David L Thomas
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
- Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology London United Kingdom
| | - John Dickson
- UCL Institute of Nuclear Medicine London United Kingdom
| | - Heidi Murray‐Smith
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Tamar Freiberger
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Andrew Wong
- MRC Unit for Lifelong Health and Ageing at UCL London United Kingdom
| | - Sebastian J Crutch
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - Marcus Richards
- MRC Unit for Lifelong Health and Ageing at UCL London United Kingdom
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
- UK Dementia Research Institute, UCL London United Kingdom
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology London United Kingdom
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41
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Abstract
In 2019, more than 4 years after the widespread availability of safe, oral, curative treatments, an estimated 58 million people were living with hepatitis C virus infections (PLWHC). Additional tools may enable those not yet reached to be treated. One such tool could be long-acting parenteral formulations of HCV treatments, which may allow PLWHC to be diagnosed and cured in a single encounter. Although existing highly effective oral medications might be formulated as long-acting parenteral treatments, pharmacological, regulatory, patent, and medical challenges have to be overcome; this requires the concerted efforts of PLWHC, researchers, funding agencies, industry, the World Health Organization, and other stakeholders.
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Affiliation(s)
- David L Thomas
- Department of Medicine, Division of Infectious Diseases, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Andrew Owen
- Department of Pharmacology and Therapeutics, Centre of Excellence for Long-acting Therapeutics (CELT), University of Liverpool, Liverpool, United Kingdom
| | - Jennifer J Kiser
- Department of Pharmaceutical Sciences, University of Colorado, Aurora, Colorado, USA
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42
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Thomas DL, Kiser JJ, Baum MM. Long-Acting Treatments for Hepatitis B. Clin Infect Dis 2022; 75:S517-S524. [PMID: 36410388 PMCID: PMC9678382 DOI: 10.1093/cid/ciac718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
There are an estimated 257 million persons living with chronic hepatitis B for whom there are multiple potential applications of long-acting antiviral compounds. Current efforts include both injection and implant approaches to formulating derivates of existing anti-HBV compounds such as tenofovir or entecavir. Substantial progress has already occurred especially as aligned with the development of long-acting tenofovir-based medications with dual activity against human immunodeficiency virus (HIV) and hepatitis B virus (HBV). Nonetheless, substantial challenges will need to be overcome before these agents are available.
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Affiliation(s)
- David L Thomas
- Department of Medicine, Division of Infectious Diseases, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jennifer J Kiser
- Department of Pharmaceutical Sciences, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, USA
| | - Marc M Baum
- Department of Chemistry, Oak Crest Institute of Science, Monrovia, California, USA
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43
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Flexner C, Thomas DL, Clayden P, Swindells S. What Clinicians Need to Know About the Development of Long-Acting Formulations. Clin Infect Dis 2022; 75:S487-S489. [PMID: 36410382 PMCID: PMC10200319 DOI: 10.1093/cid/ciac749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
We encourage readers of this Supplement to find articles that are relevant to the current and future practice of infectious diseases medicine. Access to long-acting products and formulations in low- and middle-income countries remains a key determinant of the impact of these advances on global health.
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Affiliation(s)
- Charles Flexner
- Divisions of Clinical Pharmacology, School of Medicine and Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Infectious Diseases, School of Medicine and Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - David L Thomas
- Infectious Diseases, School of Medicine and Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
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44
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Hernandez‐Garcia L, Aramendía‐Vidaurreta V, Bolar DS, Dai W, Fernández‐Seara MA, Guo J, Madhuranthakam AJ, Mutsaerts H, Petr J, Qin Q, Schollenberger J, Suzuki Y, Taso M, Thomas DL, van Osch MJP, Woods J, Zhao MY, Yan L, Wang Z, Zhao L, Okell TW. Recent Technical Developments in ASL: A Review of the State of the Art. Magn Reson Med 2022; 88:2021-2042. [PMID: 35983963 PMCID: PMC9420802 DOI: 10.1002/mrm.29381] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 05/31/2022] [Accepted: 06/18/2022] [Indexed: 12/11/2022]
Abstract
This review article provides an overview of a range of recent technical developments in advanced arterial spin labeling (ASL) methods that have been developed or adopted by the community since the publication of a previous ASL consensus paper by Alsop et al. It is part of a series of review/recommendation papers from the International Society for Magnetic Resonance in Medicine Perfusion Study Group. Here, we focus on advancements in readouts and trajectories, image reconstruction, noise reduction, partial volume correction, quantification of nonperfusion parameters, fMRI, fingerprinting, vessel selective ASL, angiography, deep learning, and ultrahigh field ASL. We aim to provide a high level of understanding of these new approaches and some guidance for their implementation, with the goal of facilitating the adoption of such advances by research groups and by MRI vendors. Topics outside the scope of this article that are reviewed at length in separate articles include velocity selective ASL, multiple-timepoint ASL, body ASL, and clinical ASL recommendations.
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Affiliation(s)
| | | | - Divya S. Bolar
- Center for Functional Magnetic Resonance Imaging, Department of RadiologyUniversity of California at San DiegoSan DiegoCaliforniaUSA
| | - Weiying Dai
- Department of Computer ScienceState University of New York at BinghamtonBinghamtonNYUSA
| | | | - Jia Guo
- Department of BioengineeringUniversity of California RiversideRiversideCaliforniaUSA
| | | | - Henk Mutsaerts
- Department of Radiology & Nuclear MedicineAmsterdam University Medical Center, Amsterdam NeuroscienceAmsterdamThe Netherlands
| | - Jan Petr
- Helmholtz‐Zentrum Dresden‐RossendorfInstitute of Radiopharmaceutical Cancer ResearchDresdenGermany
| | - Qin Qin
- The Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins UniversityBaltimoreMarylandUSA
| | | | - Yuriko Suzuki
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
| | - Manuel Taso
- Division of MRI research, RadiologyBeth Israel Deaconess Medical Center and Harvard Medical SchoolBostonMassachusettsUSA
| | - David L. Thomas
- Department of Brain Repair and RehabilitationUCL Queen Square Institute of NeurologyLondonUnited Kingdom
| | - Matthias J. P. van Osch
- C.J. Gorter Center for high field MRI, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
| | - Joseph Woods
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
- Department of RadiologyUniversity of CaliforniaLa JollaCaliforniaUSA
| | - Moss Y. Zhao
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Lirong Yan
- Department of Radiology, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear MedicineUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument ScienceZhejiang UniversityZhejiangPeople's Republic of China
| | - Thomas W. Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUnited Kingdom
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45
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Sherman KE, Thomas DL. HIV and liver disease: a comprehensive update. Top Antivir Med 2022; 30:547-558. [PMID: 36375129 PMCID: PMC9681142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Despite substantial advances in the field, liver disease morbidity and mortality remain serious issues among people with HIV. The causes of liver disease are often multifactorial and include hepatitis viruses, hepatic steatosis and oxidative stress, bacterial translocation with activation of hepatic macrophages and stellate cells, and direct toxicities from alcohol and drugs of abuse. Biopsychosocial factors including a high prevalence of psychiatric disorders, food insecurity, insufficient access to care and medications, and social stigma all play roles in the persistence of liver injury and hepatic fibrosis development among people with HIV. Rising rates of hepatocellular carcinoma have been observed, suggesting that the epidemiology of liver disease is evolving.
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Affiliation(s)
- Kenneth E. Sherman
- 1University of Cincinnati College of Medicine, Cincinnati, Ohio,Send correspondence to Kenneth E. Sherman, MD, PhD, University of Cincinnati College of Medicine, 231 Albert Sabin Way, Cincinnati, OH, 45267, or email Kenneth.
| | - David L. Thomas
- 2Johns Hopkins University School of Medicine, Baltimore, Maryland
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46
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Clement P, Castellaro M, Okell TW, Thomas DL, Vandemaele P, Elgayar S, Oliver-Taylor A, Kirk T, Woods JG, Vos SB, Kuijer JPA, Achten E, van Osch MJP, Detre JA, Lu H, Alsop DC, Chappell MA, Hernandez-Garcia L, Petr J, Mutsaerts HJMM. ASL-BIDS, the brain imaging data structure extension for arterial spin labeling. Sci Data 2022; 9:543. [PMID: 36068231 PMCID: PMC9448788 DOI: 10.1038/s41597-022-01615-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 08/05/2022] [Indexed: 11/29/2022] Open
Abstract
Arterial spin labeling (ASL) is a non-invasive MRI technique that allows for quantitative measurement of cerebral perfusion. Incomplete or inaccurate reporting of acquisition parameters complicates quantification, analysis, and sharing of ASL data, particularly for studies across multiple sites, platforms, and ASL methods. There is a strong need for standardization of ASL data storage, including acquisition metadata. Recently, ASL-BIDS, the BIDS extension for ASL, was developed and released in BIDS 1.5.0. This manuscript provides an overview of the development and design choices of this first ASL-BIDS extension, which is mainly aimed at clinical ASL applications. Discussed are the structure of the ASL data, focussing on storage order of the ASL time series and implementation of calibration approaches, unit scaling, ASL-related BIDS fields, and storage of the labeling plane information. Additionally, an overview of ASL-BIDS compatible conversion and ASL analysis software and ASL example datasets in BIDS format is provided. We anticipate that large-scale adoption of ASL-BIDS will improve the reproducibility of ASL research.
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Affiliation(s)
- Patricia Clement
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium.
| | - Marco Castellaro
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Thomas W Okell
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - David L Thomas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, UK.,Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, UK
| | | | - Sara Elgayar
- Faculty of computers and information science, Ain Shams University, Cairo, Egypt
| | | | - Thomas Kirk
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.,Sir Peter Mansfield Imaging Center, School of Medicine, University of Nottingham, Nottingham, UK
| | - Joseph G Woods
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Center for Functional Magnetic Resonance Imaging, Department of Radiology, University of California San Diego, La Jolla, California, USA
| | - Sjoerd B Vos
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, Queen Square, London, UK.,Centre for Medical Image Computing, University College London, London, UK
| | - Joost P A Kuijer
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Eric Achten
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Matthias J P van Osch
- C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - John A Detre
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Hanzhang Lu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - David C Alsop
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Michael A Chappell
- Wellcome Centre for Integrative Neuroimaging, FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.,Sir Peter Mansfield Imaging Center, School of Medicine, University of Nottingham, Nottingham, UK.,Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK.,Nottingham Biomedical Research Centre, Queens Medical Centre, University of Nottingham, Nottingham, UK
| | | | - Jan Petr
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands.,Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | - Henk J M M Mutsaerts
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium.,Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam Neuroscience, Amsterdam, The Netherlands
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47
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Perera C, Tolomeo D, Baker RR, Ohene Y, Korsak A, Lythgoe MF, Thomas DL, Wells JA. Investigating changes in blood-cerebrospinal fluid barrier function in a rat model of chronic hypertension using non-invasive magnetic resonance imaging. Front Mol Neurosci 2022; 15:964632. [PMID: 36117909 PMCID: PMC9478509 DOI: 10.3389/fnmol.2022.964632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/02/2022] [Indexed: 12/03/2022] Open
Abstract
Chronic hypertension is a major risk factor for the development of neurodegenerative disease, yet the etiology of hypertension-driven neurodegeneration remains poorly understood. Forming a unique interface between the systemic circulation and the brain, the blood-cerebrospinal fluid barrier (BCSFB) at the choroid plexus (CP) has been proposed as a key site of vulnerability to hypertension that may initiate downstream neurodegenerative processes. However, our ability to understand BCSFB's role in pathological processes has, to date, been restricted by a lack of non-invasive functional measurement techniques. In this work, we apply a novel Blood-Cerebrospinal Fluid Barrier Arterial Spin Labeling (BCSFB-ASL) Magnetic resonance imaging (MRI) approach with the aim of detecting possible derangement of BCSFB function in the Spontaneous Hypertensive Rat (SHR) model using a non-invasive, translational technique. SHRs displayed a 36% reduction in BCSFB-mediated labeled arterial water delivery into ventricular cerebrospinal fluid (CSF), relative to normotensive controls, indicative of down-regulated choroid plexus function. This was concomitant with additional changes in brain fluid biomarkers, namely ventriculomegaly and changes in CSF composition, as measured by T1 lengthening. However, cortical cerebral blood flow (CBF) measurements, an imaging biomarker of cerebrovascular health, revealed no measurable change between the groups. Here, we provide the first demonstration of BCSFB-ASL in the rat brain, enabling non-invasive assessment of BCSFB function in healthy and hypertensive rats. Our data highlights the potential for BCSFB-ASL to serve as a sensitive early biomarker for hypertension-driven neurodegeneration, in addition to investigating the mechanisms relating hypertension to neurodegenerative outcomes.
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Affiliation(s)
- Charith Perera
- Division of Medicine, UCL Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom
| | - Daniele Tolomeo
- Division of Medicine, UCL Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom
| | - Rebecca R. Baker
- Division of Medicine, UCL Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom
| | - Yolanda Ohene
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, United Kingdom
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, United Kingdom
| | - Alla Korsak
- Centre for Cardiovascular and Metabolic Neuroscience, Neuroscience, Physiology and Pharmacology, University College London, London, United Kingdom
| | - Mark F. Lythgoe
- Division of Medicine, UCL Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom
| | - David L. Thomas
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, United Kingdom
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, United Kingdom
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jack A. Wells
- Division of Medicine, UCL Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom
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48
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Patrone PN, Bedekar P, Pisanic N, Manabe YC, Thomas DL, Heaney CD, Kearsley AJ. Optimal decision theory for diagnostic testing: Minimizing indeterminate classes with applications to saliva-based SARS-CoV-2 antibody assays. Math Biosci 2022; 351:108858. [PMID: 35714754 PMCID: PMC9195412 DOI: 10.1016/j.mbs.2022.108858] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 06/03/2022] [Accepted: 06/04/2022] [Indexed: 11/17/2022]
Abstract
In diagnostic testing, establishing an indeterminate class is an effective way to identify samples that cannot be accurately classified. However, such approaches also make testing less efficient and must be balanced against overall assay performance. We address this problem by reformulating data classification in terms of a constrained optimization problem that (i) minimizes the probability of labeling samples as indeterminate while (ii) ensuring that the remaining ones are classified with an average target accuracy X. We show that the solution to this problem is expressed in terms of a bathtub-type principle that holds out those samples with the lowest local accuracy up to an X-dependent threshold. To illustrate the usefulness of this analysis, we apply it to a multiplex, saliva-based SARS-CoV-2 antibody assay and demonstrate up to a 30 % reduction in the number of indeterminate samples relative to more traditional approaches.
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Affiliation(s)
- Paul N Patrone
- National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA.
| | - Prajakta Bedekar
- National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA; Johns Hopkins University, Department of Applied Mathematics and Statistics, USA
| | - Nora Pisanic
- Johns Hopkins University, Bloomberg School of Public Health, USA
| | | | | | | | - Anthony J Kearsley
- National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD 20899, USA
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49
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Nakanjako D, Castelnuovo B, Sewankambo N, Kakaire T, Brough RL, Katabira ET, Thomas DL, Quinn TC, Colebunders R, Greene WC, Ronald AR, Coutinho A, McAdam K, Serwadda D, Wabwire-Mangen F, Katongole-Mbidde E, Musoke P, Joloba M, McKinnell H, Kamya M, Mayanja-Kizza H, Manabe YC, Kambugu A. Infectious Diseases Institute at Makerere University College of Health Sciences: a case study of a sustainable capacity building model for health care, research and training. Afr Health Sci 2022; 22:1-10. [PMID: 36321127 PMCID: PMC9590334 DOI: 10.4314/ahs.v22i2.3s] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The Infectious Diseases Institute (IDI), established in 2001, was the first autonomous institution of Makerere University set up as an example of what self-governing institutes can do in transforming the academic environment to become a rapidly progressive University addressing the needs of society This paper describes the success factors and lessons learned in development of sustainable centers of excellence to prepare academic institutions to respond appropriately to current and future challenges to global health. Key success factors included a) strong collaboration by local and international experts to combat the HIV pandemic, along with b) seed funding from Pfizer Inc., c) longstanding collaboration with Accordia Global Health Foundation to create and sustain institutional strengthening programs, d) development of a critical mass of multi-disciplinary research leaders and managers of the center, and e) a series of strong directors who built strong governance structures to execute the vision of the institute, with subsequent transition to local leadership.
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Affiliation(s)
- Damalie Nakanjako
- School of Medicine, Makerere University College of Heath Sciences, Kampala, Uganda
| | - Barbara Castelnuovo
- Infectious Diseases Institute, Makerere University College of Heath Sciences, Kampala, Uganda
| | - Nelson Sewankambo
- School of Medicine, Makerere University College of Heath Sciences, Kampala, Uganda
| | - Tom Kakaire
- Infectious Diseases Institute, Makerere University College of Heath Sciences, Kampala, Uganda
| | - Richard L Brough
- Former Executive Director, Infectious Diseases Institute, Kampala, Uganda
| | - Elly T Katabira
- School of Medicine, Makerere University College of Heath Sciences, Kampala, Uganda
| | - David L Thomas
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine
| | - Thomas C Quinn
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | | | - Warner C Greene
- Gladstone Institute of Virolology and Immunology, University of California, San Francisco, USA
| | - Allan R Ronald
- University of Manitoba, 99 Wellington Crescent, Winnipeg Manitoba, R3M0A2, Canada
| | - Alex Coutinho
- Former Executive Director, Infectious Diseases Institute, Kampala, Uganda
| | - Keith McAdam
- University of Manitoba, 99 Wellington Crescent, Winnipeg Manitoba, R3M0A2, Canada
| | - David Serwadda
- School of Public Health, Makerere University College of Heath Sciences, Kampala, Uganda
| | - Fred Wabwire-Mangen
- School of Public Health, Makerere University College of Heath Sciences, Kampala, Uganda
| | | | - Philippa Musoke
- School of Medicine, Makerere University College of Heath Sciences, Kampala, Uganda
| | - Moses Joloba
- School of Biomedical Sciences, Makerere University College of Heath Sciences, Kampala, Uganda
| | - Henry McKinnell
- Former Chairman, Moody's corporation, Former Chairman and CEO Optimer Pharmaceuticals and Former Chairman and CEO Pfizer Inc
| | - Moses Kamya
- School of Medicine, Makerere University College of Heath Sciences, Kampala, Uganda
| | | | - Yukari C Manabe
- Infectious Diseases Institute, Makerere University College of Heath Sciences, Kampala, Uganda
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine
| | - Andrew Kambugu
- Infectious Diseases Institute, Makerere University College of Heath Sciences, Kampala, Uganda
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50
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Demko ZO, Yu T, Mullapudi SK, Varela Heslin MG, Dorsey CA, Payton CB, Tornheim JA, Blair PW, Mehta SH, Thomas DL, Manabe YC, Antar AAR. Post-acute sequelae of SARS-CoV-2 (PASC) impact quality of life at 6, 12 and 18 months post-infection. medRxiv 2022:2022.08.08.22278543. [PMID: 35982674 PMCID: PMC9387157 DOI: 10.1101/2022.08.08.22278543] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Little data exist on long COVID outcomes beyond one year. In a cohort enrolled with mild-moderate acute COVID-19, a wide range of symptoms manifest at 6, 12, and 18 months. Endorsing over 3 symptoms associates with poorer quality of life in 5 domains: physical, social, fatigue, pain, and general health.
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