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Stige KE, Kverneng SU, Sharma S, Skeie GO, Sheard E, Søgnen M, Geijerstam SA, Vetås T, Wahlvåg AG, Berven H, Buch S, Reese D, Babiker D, Mahdi Y, Wade T, Miranda GP, Ganguly J, Tamilselvam YK, Chai JR, Bansal S, Aur D, Soltani S, Adams S, Dölle C, Dick F, Berntsen EM, Grüner R, Brekke N, Riemer F, Goa PE, Haugarvoll K, Haacke EM, Jog M, Tzoulis C. The STRAT-PARK cohort: A personalized initiative to stratify Parkinson's disease. Prog Neurobiol 2024; 236:102603. [PMID: 38604582 DOI: 10.1016/j.pneurobio.2024.102603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/15/2024] [Accepted: 04/07/2024] [Indexed: 04/13/2024]
Abstract
The STRAT-PARK initiative aims to provide a platform for stratifying Parkinson's disease (PD) into biological subtypes, using a bottom-up, multidisciplinary biomarker-based and data-driven approach. PD is a heterogeneous entity, exhibiting high interindividual clinicopathological variability. This diversity suggests that PD may encompass multiple distinct biological entities, each driven by different molecular mechanisms. Molecular stratification and identification of disease subtypes is therefore a key priority for understanding and treating PD. STRAT-PARK is a multi-center longitudinal cohort aiming to recruit a total of 2000 individuals with PD and neurologically healthy controls from Norway and Canada, for the purpose of identifying molecular disease subtypes. Clinical assessment is performed annually, whereas biosampling, imaging, and digital and neurophysiological phenotyping occur every second year. The unique feature of STRAT-PARK is the diversity of collected biological material, including muscle biopsies and platelets, tissues particularly useful for mitochondrial biomarker research. Recruitment rate is ∼150 participants per year. By March 2023, 252 participants were included, comprising 204 cases and 48 controls. STRAT-PARK is a powerful stratification initiative anticipated to become a global research resource, contributing to personalized care in PD.
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Affiliation(s)
- Kjersti Eline Stige
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway; The Department of Neuromedicine and Movement Sciences, Norwegian University of Science and Technology, Trondheim 7491, Norway; Department of Neurology and Clinical Neurophysiology, St Olav's University Hospital, Trondheim 7006, Norway
| | - Simon Ulvenes Kverneng
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Soumya Sharma
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Geir-Olve Skeie
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Erika Sheard
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Mona Søgnen
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Solveig Af Geijerstam
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Therese Vetås
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Anne Grete Wahlvåg
- Department of Neurology and Clinical Neurophysiology, St Olav's University Hospital, Trondheim 7006, Norway
| | - Haakon Berven
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Sagar Buch
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - David Reese
- Imaging Research Laboratories, Robarts Research Institute, Ontario, London N6A 5B7, Canada
| | - Dina Babiker
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Yekta Mahdi
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Trevor Wade
- Department of Medical Biophysics, Robarts Research Institute, Schulich School of Medicine and Dentistry, Western University, Ontario, London N6A 6B7, Canada
| | - Gala Prado Miranda
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Jacky Ganguly
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Yokhesh Krishnasamy Tamilselvam
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada; Department of Electrical and Computer Engineering, Canadian Surgical Technologies and Advanced Robotics (CSTAR), University of Western Ontario (UWO), Ontario, London, Canada
| | - Jia Ren Chai
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Saurabh Bansal
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Dorian Aur
- Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Sima Soltani
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Scott Adams
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada; School of Communication Sciences & Disorders, Faculty of Health Sciences, Western University, Canada
| | - Christian Dölle
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Fiona Dick
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway
| | - Erik Magnus Berntsen
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim 7006, Norway; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Renate Grüner
- Department of Physics and Technology, University of Bergen, Bergen 5007, Norway; Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Post Office Box 1400, Bergen 5021, Norway
| | - Njål Brekke
- Department of Physics and Technology, University of Bergen, Bergen 5007, Norway; Radiology Department, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - Frank Riemer
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Post Office Box 1400, Bergen 5021, Norway
| | - Pål Erik Goa
- Department of Radiology and Nuclear Medicine, St. Olav's University Hospital, Trondheim 7006, Norway; Department of Physics, Norwegian University of Science and Technology, Trondheim 7491, Norway
| | - Kristoffer Haugarvoll
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway
| | - E Mark Haacke
- Department of Neurology, Wayne State University School of Medicine, Detroit, Michigan, USA; Department of Radiology, Wayne State University School of Medicine, Detroit, Michigan, USA
| | - Mandar Jog
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Neurological Sciences, London Health Sciences Centre, Western University, London, ON N6A 5A5, Canada
| | - Charalampos Tzoulis
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Jonas Lies vei 65, Bergen 5021, Norway; Department of Clinical Medicine, University of Bergen, Pb 7804, Bergen 5020, Norway; K.G. Jebsen Center for Translational Research in Parkinson's disease, University of Bergen, Pb 7804, Bergen 5020, Norway.
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Weintraub D, Marras C, Amara A, Anderson KE, Chahine LM, Eberly S, Hosamath A, Kinel D, Mantri S, Mathur S, Oakes D, Purks JL, Standaert DG, Shoulson I, Arbatti L. Association between Subjective Cognitive Complaints and Incident Functional Impairment in Parkinson's Disease. Mov Disord 2024; 39:706-714. [PMID: 38318953 DOI: 10.1002/mds.29725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/05/2024] [Accepted: 01/09/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Early identification of subjective cognitive complaints (SCC) in Parkinson's disease (PD) may improve patient care if it predicts cognition-related functional impairment (CFI). OBJECTIVES The aim was to determine the cross-sectional and longitudinal association between SCC and CFI in PD. METHODS Data were obtained from Fox Insight, an online longitudinal study that collects PD patient-reported outcomes. Participants completed a PD Patient Report of Problems that asked participants for their five most bothersome disease problems. SCCs were placed into eight categories through human-in-the-loop curation and classification. CFI had a Penn Parkinson's Daily Activities Questionnaire (PDAQ-15) score ≤49. Cox proportional hazards models and Kaplan-Meier survival analyses determined if baseline SCC was associated with incident CFI. RESULTS The PD-PROP cohort (N = 21,160) was 55.8% male, mean age was 65.9 years, and PD duration was 4.8 years. At baseline, 31.9% (N = 6750) of participants reported one or more SCCs among their five most bothersome problems, including memory (13.2%), language/word finding (12.5%), and concentration/attention (9.6%). CFI occurred in 34.7% (N = 7332) of participants. At baseline, SCC was associated with CFI (P-value <0.001). SCC at baseline was associated with incident CFI (hazard ratio [HR] = 1.58 [95% confidence interval: 1.45, 1.72], P-value <0.001), as did cognitive impairment not otherwise specified (HR = 2.31), executive abilities (HR = 1.97), memory (HR = 1.85), and cognitive slowing (HR = 1.77) (P-values <0.001). Kaplan-Meier curves showed that by year 3 an estimated 45% of participants with any SCC at baseline developed new-onset CFI. CONCLUSIONS Self-reported bothersome cognitive complaints are associated with new-onset CFI in PD. Remote electronic assessment can facilitate widespread use of patient self-report at population scale. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Daniel Weintraub
- Departments of Psychiatry and Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Connie Marras
- Edmond J. Safra Program in Parkinson's Disease, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Amy Amara
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Karen E Anderson
- Departments of Psychiatry and Neurology, Georgetown University, Washington, DC, USA
| | - Lana M Chahine
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Shirley Eberly
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA
| | - Abhishek Hosamath
- Grey Matter Technologies, a Wholly Owned Subsidiary of modality.ai, San Francisco, California, USA
| | - Daniel Kinel
- Center for Health + Technology, University of Rochester Medical Center, Rochester, New York, USA
| | - Sneha Mantri
- Department of Neurology, Duke University, Durham, North Carolina, USA
| | | | - David Oakes
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA
| | - Jennifer L Purks
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - David G Standaert
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Ira Shoulson
- Grey Matter Technologies, a Wholly Owned Subsidiary of modality.ai, San Francisco, California, USA
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Lakshmi Arbatti
- Grey Matter Technologies, a Wholly Owned Subsidiary of modality.ai, San Francisco, California, USA
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Beiriger JW, Tao W, Bruce MK, Anstadt E, Christensen C, Smetona J, Whitaker R, Goldstein JA. CranioRate: An Image-Based, Deep-Phenotyping Analysis Toolset and Online Clinician Interface for Metopic Craniosynostosis. Plast Reconstr Surg 2024; 153:112e-119e. [PMID: 36943708 DOI: 10.1097/prs.0000000000010452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
BACKGROUND The diagnosis and management of metopic craniosynostosis involve subjective decision-making at the point of care. The purpose of this work was to describe a quantitative severity metric and point-of-care user interface to aid clinicians in the management of metopic craniosynostosis and to provide a platform for future research through deep phenotyping. METHODS Two machine-learning algorithms were developed that quantify the severity of craniosynostosis-a supervised model specific to metopic craniosynostosis [Metopic Severity Score (MSS)] and an unsupervised model used for cranial morphology in general [Cranial Morphology Deviation (CMD)]. Computed tomographic (CT) images from multiple institutions were compiled to establish the spectrum of severity, and a point-of-care tool was developed and validated. RESULTS Over the study period (2019 to 2021), 254 patients with metopic craniosynostosis and 92 control patients who underwent CT scanning between the ages of 6 and 18 months were included. CT scans were processed using an unsupervised machine-learning based dysmorphology quantification tool, CranioRate. The average MSS was 0.0 ± 1.0 for normal controls and 4.9 ± 2.3 ( P < 0.001) for those with metopic synostosis. The average CMD was 85.2 ± 19.2 for normal controls and 189.9 ± 43.4 ( P < 0.001) for those with metopic synostosis. A point-of-care user interface (craniorate.org) has processed 46 CT images from 10 institutions. CONCLUSIONS The resulting quantification of severity using MSS and CMD has shown an improved capacity, relative to conventional measures, to automatically classify normal controls versus patients with metopic synostosis. The authors have mathematically described, in an objective and quantifiable manner, the distribution of phenotypes in metopic craniosynostosis.
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Affiliation(s)
- Justin W Beiriger
- From the Department of Plastic Surgery, University of Pittsburgh Medical Center
| | | | - Madeleine K Bruce
- From the Department of Plastic Surgery, University of Pittsburgh Medical Center
| | - Erin Anstadt
- From the Department of Plastic Surgery, University of Pittsburgh Medical Center
| | | | - John Smetona
- From the Department of Plastic Surgery, University of Pittsburgh Medical Center
| | | | - Jesse A Goldstein
- From the Department of Plastic Surgery, University of Pittsburgh Medical Center
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Schalkamp AK, Peall KJ, Harrison NA, Sandor C. Wearable movement-tracking data identify Parkinson's disease years before clinical diagnosis. Nat Med 2023; 29:2048-2056. [PMID: 37400639 DOI: 10.1038/s41591-023-02440-2] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/05/2023] [Indexed: 07/05/2023]
Abstract
Parkinson's disease is a progressive neurodegenerative movement disorder with a long latent phase and currently no disease-modifying treatments. Reliable predictive biomarkers that could transform efforts to develop neuroprotective treatments remain to be identified. Using UK Biobank, we investigated the predictive value of accelerometry in identifying prodromal Parkinson's disease in the general population and compared this digital biomarker with models based on genetics, lifestyle, blood biochemistry or prodromal symptoms data. Machine learning models trained using accelerometry data achieved better test performance in distinguishing both clinically diagnosed Parkinson's disease (n = 153) (area under precision recall curve (AUPRC) 0.14 ± 0.04) and prodromal Parkinson's disease (n = 113) up to 7 years pre-diagnosis (AUPRC 0.07 ± 0.03) from the general population (n = 33,009) compared with all other modalities tested (genetics: AUPRC = 0.01 ± 0.00, P = 2.2 × 10-3; lifestyle: AUPRC = 0.03 ± 0.04, P = 2.5 × 10-3; blood biochemistry: AUPRC = 0.01 ± 0.00, P = 4.1 × 10-3; prodromal signs: AUPRC = 0.01 ± 0.00, P = 3.6 × 10-3). Accelerometry is a potentially important, low-cost screening tool for determining people at risk of developing Parkinson's disease and identifying participants for clinical trials of neuroprotective treatments.
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Affiliation(s)
- Ann-Kathrin Schalkamp
- Division of Psychological Medicine and Clinical Neuroscience, UK Dementia Research Institute, Cardiff University, Cardiff, UK
| | - Kathryn J Peall
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, UK
| | - Neil A Harrison
- Division of Psychological Medicine and Clinical Neurosciences, Neuroscience and Mental Health Innovation Institute, Cardiff, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff, UK
| | - Cynthia Sandor
- Division of Psychological Medicine and Clinical Neuroscience, UK Dementia Research Institute, Cardiff University, Cardiff, UK.
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Alfalahi H, Shehhi AA, Lamprou C, Ziogas I, Ganiti-Roumeliotou E, Khandoker AH, Hadjileontiadis LJ. Parkinsonian Tremor Detection with Compact Convolutional Transformer from Bispectrum Representation of tri-Axial Accelerometer Signals. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083408 DOI: 10.1109/embc40787.2023.10340646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
After the breakthroughs of Transformer networks in Natural Language Processing (NLP) tasks, they have led to exciting progress in visual tasks as well. Nonetheless, there has been a parallel growth in the number of parameters and the amount of training data, which led to the conclusion that Transformers are not suited for small datasets. This paper is the first to convey the feasibility of Compact Convolutional Transformers (CCT) for the prediction of Parkinsonian postural tremor based on the Bispectrum (BS) representation of IMU accelerometer time series. The dataset includes tri-axial accelerometer signals collected unobtrusively in-the-wild while subjects are on a phone call, and labelled by neurologists and signal processing experts. The BS is a noise-immune, higher-order representation that reflects a signal's deviation from Gaussianity and measures quadratic phase coupling. We performed comparative classification experiments using the CCT, pre-trained CNNs such as VGG-16 and ResNet-50, and the conventional Vision Transformer (ViT). Our model achieves competitive prediction accuracy and F1 score of 96% with only 1.016 M trainable parameters, compared to the ViT with 21.659 M trainable parameters, in a five-fold cross-validation scheme. Our model also outperforms pre-trained CNNs such as VGG-16 and ResNet-50. Furthermore, we show that the performance gains are maintained when training on a larger dataset of BS images. Our effort here is motivated by the hypothesis that data-efficient transformers outperform transfer learning using pre-trained CNNs, paving the way for promising deep learning architecture for small-scale, novel and noisy medical imaging datasets.Clinical relevance- Novel deep learning model for unobtrusive prediction of Parkinsonian Postural Tremor from Bispectrum image representation of tri-axial accelerometer signals collected in-the-wild.
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Callahan TJ, Stefanski AL, Wyrwa JM, Zeng C, Ostropolets A, Banda JM, Baumgartner WA, Boyce RD, Casiraghi E, Coleman BD, Collins JH, Deakyne Davies SJ, Feinstein JA, Lin AY, Martin B, Matentzoglu NA, Meeker D, Reese J, Sinclair J, Taneja SB, Trinkley KE, Vasilevsky NA, Williams AE, Zhang XA, Denny JC, Ryan PB, Hripcsak G, Bennett TD, Haendel MA, Robinson PN, Hunter LE, Kahn MG. Ontologizing health systems data at scale: making translational discovery a reality. NPJ Digit Med 2023; 6:89. [PMID: 37208468 PMCID: PMC10196319 DOI: 10.1038/s41746-023-00830-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 04/28/2023] [Indexed: 05/21/2023] Open
Abstract
Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.
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Affiliation(s)
- Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - Adrianne L Stefanski
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Jordan M Wyrwa
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Chenjie Zeng
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA
| | - William A Baumgartner
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260, USA
| | - Elena Casiraghi
- Computer Science, Università degli Studi di Milano, Milan, Italy
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Ben D Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Janine H Collins
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Sara J Deakyne Davies
- Department of Research Informatics & Data Science, Analytics Resource Center, Children's Hospital Colorado, Aurora, CO, 80045, USA
| | - James A Feinstein
- Adult and Child Center for Health Outcomes Research and Delivery Science (ACCORDS), University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Asiyah Y Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Blake Martin
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | | | | | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Sanya B Taneja
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Katy E Trinkley
- Department of Family Medicine, University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Nicole A Vasilevsky
- Translational and Integrative Sciences Lab, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Andrew E Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Tufts University, Boston, MA, 02155, USA
| | - Xingmin A Zhang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patrick B Ryan
- Janssen Research and Development, Raritan, NJ, 08869, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Tellen D Bennett
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
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7
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Giuliano C, Cerri S, Cesaroni V, Blandini F. Relevance of Biochemical Deep Phenotyping for a Personalised Approach to Parkinson's Disease. Neuroscience 2023; 511:100-109. [PMID: 36572171 DOI: 10.1016/j.neuroscience.2022.12.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 10/05/2022] [Accepted: 12/19/2022] [Indexed: 12/25/2022]
Abstract
Parkinson's disease (PD) is a multifactorial neurodegenerative disorder characterised by the progressive loss of dopaminergic neurons in the nigrostriatal tract. The identification of disease-modifying therapies is the Holy Grail of PD research, but to date no drug has been approved as such a therapy. A possible reason is the remarkable phenotypic heterogeneity of PD patients, which can generate confusion in the interpretation of results or even mask the efficacy of a therapeutic intervention. This heterogeneity should be taken into account in clinical trials, stratifying patients by their expected response to drugs designed to engage selected molecular targets. In this setting, stratification methods (clinical and genetic) should be supported by biochemical phenotyping of PD patients, in line with the deep phenotyping concept. Collection, from single patients, of a range of biological samples would streamline the generation of these profiles. Several studies have proposed biochemical characterisations of patient cohorts based on analysis of blood, cerebrospinal fluid, urine, stool, saliva and skin biopsy samples, with extracellular vesicles attracting increasing interest as a source of biomarkers. In this review we report and critically discuss major studies that used a biochemical approach to stratify their PD cohorts. The analyte most studied is α-synuclein, while other studies have focused on neurofilament light chain, lysosomal proteins, inflammasome-related proteins, LRRK2 and the urinary proteome. At present, stratification of PD patients, while promising, is still a nascent approach. Deep phenotyping of patients will allow clinical researchers to identify homogeneous subgroups for the investigation of tailored disease-modifying therapies, enhancing the chances of therapeutic success.
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Affiliation(s)
- Claudio Giuliano
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Silvia Cerri
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Valentina Cesaroni
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - Fabio Blandini
- Unit of Cellular and Molecular Neurobiology, IRCCS Mondino Foundation, 27100 Pavia, Italy; Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy.
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8
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Jensen-Roberts S, Myers TL, Auinger P, Cannon P, Rowbotham HM, Coker D, Chanoff E, Soto J, Pawlik M, Amodeo K, Sharma S, Valdovinos B, Wilson R, Sarkar A, McDermott MP, Alcalay RN, Biglan K, Kinel D, Tanner C, Winter-Evans R, Augustine EF, Holloway RG, Dorsey ER, Schneider RB. A Remote Longitudinal Observational Study of Individuals at Genetic Risk for Parkinson Disease. Neurol Genet 2022; 8:e200008. [PMID: 35966918 PMCID: PMC9372873 DOI: 10.1212/nxg.0000000000200008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/09/2022] [Indexed: 11/15/2022]
Abstract
Background and Objectives To recruit and characterize a national cohort of individuals who have a genetic variant (LRRK2 G2019S) that increases risk of Parkinson disease (PD), assess participant satisfaction with a decentralized, remote research model, and evaluate interest in future clinical trials. Methods In partnership with 23andMe, Inc., a personal genetics company, LRRK2 G2019S carriers with and without PD were recruited to participate in an ongoing 36-month decentralized, remote natural history study. We examined concordance between self-reported and clinician-determined PD diagnosis. We applied the Movement Disorder Society Prodromal Parkinson's Disease Criteria and asked investigators to identify concern for parkinsonism to distinguish participants with probable prodromal PD. We compared baseline characteristics of LRRK2 G2019S carriers with PD, with prodromal PD, and without PD. Results Over 15 months, we enrolled 277 LRRK2 G2019S carriers from 34 states. At baseline, 60 had self-reported PD (mean [SD] age 67.8 years [8.4], 98% White, 52% female, 80% Ashkenazi Jewish, and 67% with a family history of PD), and 217 did not (mean [SD] age 53.7 years [15.1], 95% White, 59% female, 73% Ashkenazi Jewish, and 57% with a family history of PD). Agreement between self-reported and clinician-determined PD status was excellent (κ = 0.94, 95% confidence interval 0.89–0.99). Twenty-four participants had prodromal PD; 9 met criteria for probable prodromal PD and investigators identified concern for parkinsonism in 20 cases. Compared with those without prodromal PD, participants with prodromal PD were older (63.9 years [9.0] vs 51.9 years [15.1], p < 0.001), had higher modified Movement Disorders Society-Unified Parkinson's Disease Rating Scale motor scores (5.7 [4.3] vs 0.8 [2.1], p < 0.001), and had higher Scale for Outcomes in PD for Autonomic Symptoms scores (11.5 [6.2] vs 6.9 [5.7], p = 0.002). Two-thirds of participants enrolled were new to research, 97% were satisfied with the overall study, and 94% of those without PD would participate in future preventive clinical trials. Discussion An entirely remote national cohort of LRRK2 G2019S carriers was recruited from a single site. This study will prospectively characterize a large LRRK2 G2019S cohort, refine a new model of clinical research, and engage new research participants willing to participate in future therapeutic trials.
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Xu Z, Shen B, Tang Y, Wu J, Wang J. Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:349-361. [PMID: 36939759 PMCID: PMC9590510 DOI: 10.1007/s43657-022-00051-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/12/2022] [Accepted: 03/28/2022] [Indexed: 11/27/2022]
Abstract
Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.
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Affiliation(s)
- Zhiheng Xu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Bo Shen
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Yilin Tang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jianjun Wu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jian Wang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
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10
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Shoulson I, Arbatti L, Hosamath A, Eberly SW, Oakes D. Longitudinal Cohort Study of Verbatim-Reported Postural Instability Symptoms as Outcomes for Online Parkinson’s Disease Trials. JOURNAL OF PARKINSON'S DISEASE 2022; 12:1969-1978. [PMID: 35694935 PMCID: PMC9535582 DOI: 10.3233/jpd-223274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The Parkinson’s Disease Patient Report of Problems (PD-PROP) captures the problems and functional impact that patients report verbatim. Online research participation and advances in language analysis have enabled longitudinal collection and classification of symptoms as trial outcomes. Objective: Analyze verbatim reports longitudinally to examine postural-instability symptoms as 1) precursors of subsequent falling and 2) newly occurring symptoms that could serve as outcome measures in randomized controlled trials. Methods: Problems reported by >25,000 PD patients in their own words were collected online in the Fox Insight observational study and classified into symptoms by natural language processing, clinical curation, and machine learning. Symptoms of gait, balance, falling, and freezing and associated reports of having fallen in the last month were analyzed over three years of longitudinal observation by a Cox regression model in a cohort of 8,287 participants. New onset of gait, balance, falling, and freezing symptoms was analyzed by Kaplan-Meier survival techniques in 4,119 participants who had not previously reported these symptoms. Results: Classified verbatim symptoms of postural instability were significant precursors of subsequent falling among participants who were older, female, and had longer PD duration. New onset of symptoms steadily increased and informed sample size estimates for clinical trials to reduce the onset of these symptoms. Conclusion: The tools to analyze symptoms reported by PD patients in their own words and capacity to enroll large numbers of research participants online support the feasibility and statistical power for conducting randomized clinical trials to detect effects of therapeutic interventions.
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Affiliation(s)
- Ira Shoulson
- Department of Neurology, University of Rochester, Rochester, NY, USA
- Grey Matter Technologies, Inc., Longboat Key, FL, USA
| | | | | | - Shirley W. Eberly
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA
| | - David Oakes
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA
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11
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Hecker P, Steckhan N, Eyben F, Schuller BW, Arnrich B. Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends. Front Digit Health 2022; 4:842301. [PMID: 35899034 PMCID: PMC9309252 DOI: 10.3389/fdgth.2022.842301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 05/25/2022] [Indexed: 11/25/2022] Open
Abstract
Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.
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Affiliation(s)
- Pascal Hecker
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
- audEERING GmbH, Gilching, Germany
- *Correspondence: Pascal Hecker ; orcid.org/0000-0001-6604-1671
| | - Nico Steckhan
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
| | | | - Björn W. Schuller
- audEERING GmbH, Gilching, Germany
- EIHW – Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- GLAM – Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
| | - Bert Arnrich
- Digital Health – Connected Healthcare, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
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12
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Schalkamp AK, Rahman N, Monzón-Sandoval J, Sandor C. Deep phenotyping for precision medicine in Parkinson's disease. Dis Model Mech 2022; 15:dmm049376. [PMID: 35647913 PMCID: PMC9178512 DOI: 10.1242/dmm.049376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.
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Affiliation(s)
| | | | | | - Cynthia Sandor
- UK Dementia Research Institute at Cardiff University,Division of Psychological Medicine and Clinical Neuroscience, Haydn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK
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13
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Mari Z, Mestre TA. The Disease Modification Conundrum in Parkinson’s Disease: Failures and Hopes. Front Aging Neurosci 2022; 14:810860. [PMID: 35296034 PMCID: PMC8920063 DOI: 10.3389/fnagi.2022.810860] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/03/2022] [Indexed: 12/11/2022] Open
Abstract
In the last half-century, Parkinson’s disease (PD) has played a historical role in demonstrating our ability to translate preclinical scientific advances in pathology and pharmacology into highly effective clinical therapies. Yet, as highly efficacious symptomatic treatments were successfully developed and adopted in clinical practice, PD remained a progressive disease without a cure. In contrast with the success story of symptomatic therapies, the lack of translation of disease-modifying interventions effective in preclinical models into clinical success has continued to accumulate failures in the past two decades. The ability to stop, prevent or mitigate progression in PD remains the “holy grail” in PD science at the present time. The large number of high-quality disease modification clinical trials in the past two decades with its lessons learned, as well as the growing knowledge of PD molecular pathology should enable us to have a deeper understanding of the reasons for past failures and what we need to do to reach better outcomes. Periodic reviews and mini-reviews of the unsolved disease modification conundrum in PD are important, considering how this field is rapidly evolving along with our views and understanding of the possible explanations.
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Affiliation(s)
- Zoltan Mari
- Parkinson’s and Movement Disorders Program, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
- *Correspondence: Zoltan Mari,
| | - Tiago A. Mestre
- Division of Neurology, Department of Medicine, Parkinson’s Disease and Movement Disorders Center, The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada
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14
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Salchow-Hömmen C, Skrobot M, Jochner MCE, Schauer T, Kühn AA, Wenger N. Review-Emerging Portable Technologies for Gait Analysis in Neurological Disorders. Front Hum Neurosci 2022; 16:768575. [PMID: 35185496 PMCID: PMC8850274 DOI: 10.3389/fnhum.2022.768575] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/07/2022] [Indexed: 01/29/2023] Open
Abstract
The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.
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Affiliation(s)
- Christina Salchow-Hömmen
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Matej Skrobot
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Magdalena C E Jochner
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Thomas Schauer
- Control Systems Group, Technische Universität Berlin, Berlin, Germany
| | - Andrea A Kühn
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Berlin School of Mind and Brain, Charité-Universitätsmedizin Berlin, Berlin, Germany
- NeuroCure Clinical Research Centre, Charité-Universitätsmedizin Berlin, Berlin, Germany
- German Center for Neurodegenerative Diseases, DZNE, Berlin, Germany
| | - Nikolaus Wenger
- Department of Neurology With Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
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15
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Saxena A, Paredes-Echeverri S, Michaelis R, Popkirov S, Perez DL. Using the Biopsychosocial Model to Guide Patient-Centered Neurological Treatments. Semin Neurol 2022; 42:80-87. [PMID: 35114695 DOI: 10.1055/s-0041-1742145] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The biopsychosocial model was defined by George L. Engel to propose a holistic approach to patient care. Through this model, physicians can understand patients in their context to aid the development of tailored, individualized treatment plans that consider relevant biological, psychological, and social-cultural-spiritual factors impacting health and longitudinal care. In this article, we advocate for the use of the biopsychosocial model in neurology practice across outpatient and inpatient clinical settings. To do so, we first present the history of the biopsychosocial model, and its relationships to precision medicine and deep phenotyping. Then, we bring the neurologist up-to-date information on the components of the biopsychosocial clinical formulation, including predisposing, precipitating, perpetuating, and protective factors. We conclude by detailing illustrative neurological case examples using the biopsychosocial model, emphasizing the importance of considering relevant psychological and social factors to aid the delivery of patient-centered clinical care in neurology.
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Affiliation(s)
- Aneeta Saxena
- Epilepsy Division, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts.,Functional Neurological Disorder Unit, Division of Cognitive Behavioral Neurology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Sara Paredes-Echeverri
- Functional Neurological Disorder Unit, Division of Cognitive Behavioral Neurology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Rosa Michaelis
- Department of Neurology, University Hospital Knappschaftskrankenhaus Bochum, Ruhr University Bochum, Bochum, Germany.,Department of Neurology, Gemeinschaftskrankenhaus Herdecke, Herdecke, Germany
| | - Stoyan Popkirov
- Department of Neurology, University Hospital Knappschaftskrankenhaus Bochum, Ruhr University Bochum, Bochum, Germany
| | - David L Perez
- Functional Neurological Disorder Unit, Division of Cognitive Behavioral Neurology, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.,Division of Neuropsychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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16
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Abstract
Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.
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Affiliation(s)
- Anoopum S. Gupta
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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17
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Riggare S, Hägglund M, Bredenoord AL, de Groot M, Bloem BR. Ethical Aspects of Personal Science for Persons with Parkinson's Disease: What Happens When Self-Tracking Goes from Selfcare to Publication? JOURNAL OF PARKINSON'S DISEASE 2022; 11:1927-1933. [PMID: 34120915 PMCID: PMC8609698 DOI: 10.3233/jpd-212647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Accepted: 05/21/2021] [Indexed: 11/29/2022]
Abstract
Using Parkinson's disease as an exemplary chronic condition, this Commentary discusses ethical aspects of using self-tracking for personal science, as compared to using self-tracking in the context of conducting clinical research on groups of study participants. Conventional group-based clinical research aims to find generalisable answers to clinical or public health questions. The aim of personal science is different: to find meaningful answers that matter first and foremost to an individual with a particular health challenge. In the case of personal science, the researcher and the participant are one and the same, which means that specific ethical issues may arise, such as the need to protect the participant against self-harm. To allow patient-led research in the form of personal science in the Parkinson field to evolve further, the development of a specific ethical framework for self-tracking for personal science is needed.
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Affiliation(s)
- Sara Riggare
- Uppsala University, Department of Women’s and Children’s Health, Healthcare Sciences and e-Health, Uppsala, Sweden
| | - Maria Hägglund
- Uppsala University, Department of Women’s and Children’s Health, Healthcare Sciences and e-Health, Uppsala, Sweden
| | - Annelien L. Bredenoord
- University Medical Center Utrecht, Utrecht University, Department of Medical Humanities, Utrecht, The Netherlands
| | - Martijn de Groot
- Radboud University Medical Centre, Health Innovation Labs, Nijmegen, The Netherlands
| | - Bastiaan R. Bloem
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
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18
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Riggare S, Stamford J, Hägglund M. A Long Way to Go: Patient Perspectives on Digital Health for Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2022; 11:S5-S10. [PMID: 33682728 PMCID: PMC8385497 DOI: 10.3233/jpd-202408] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Digital health promises to improve healthcare, health, and wellness through the use of digital technologies. The purpose of this commentary is to review and discuss the field of digital health for Parkinson’s disease (PD) focusing on the needs, expectations, and wishes of people with PD (PwP). Our analysis shows that PwP want to use digital technologies to actively manage the full complexity of living with PD on an individual level, including the unpredictability and variability of the condition. Current digital health projects focusing on PD, however, does not live up to the expectations of PwP. We conclude that for digital health to reach its full potential, the right of PwP to access their own data needs to be recognised, PwP should routinely receive personalised feedback based on their data, and active involvement of PwP as an equal partner in digital health development needs to be the norm.
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Affiliation(s)
- Sara Riggare
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Jon Stamford
- Gentleman Neuroscientist and Independent Parkinson's Patient Advocate, UK
| | - Maria Hägglund
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
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19
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Personalizing decision-making for persons with Parkinson's disease: where do we stand and what to improve? J Neurol 2022; 269:3569-3578. [PMID: 35084559 PMCID: PMC9217860 DOI: 10.1007/s00415-022-10969-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 11/05/2022]
Abstract
Background The large variety in symptoms and treatment effects across different persons with Parkinson’s disease (PD) warrants a personalized approach, ensuring that the best decision is made for each individual. We aimed to further clarify this process of personalized decision-making, from the perspective of medical professionals. Methods We audio-taped 52 consultations with PD patients and their neurologist or PD nurse-specialist, in 6 outpatient clinics. We focused coding of the transcripts on which decisions were made and on if and how decisions were personalized. We subsequently interviewed professionals to elaborate on how and why decisions were personalized, and which decisions would benefit most from a more personalized approach. Results Most decisions were related to medication, referral or lifestyle. Professionals balanced clinical factors, including individual (disease-) characteristics, and non-clinical factors, including patients’ preference, for each type of decision. These factors were often not explicitly discussed with the patient. Professionals experienced difficulties in personalizing decisions, mostly because evidence on the impact of characteristics of an individual patient on the outcome of the decision is unavailable. Categories of decisions for which professionals emphasized the importance of a more personalized perspective include choices not only for medication and advanced treatments, but also for referrals, lifestyle and diagnosis. Conclusions Clinical decision-making is a complex process, influenced by many different factors that differ for each decision and for each individual. In daily practice, it proves difficult to tailor decisions to individual (disease-) characteristics, probably because sufficient evidence on the impact of these individual characteristics on outcomes is lacking.
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20
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Jiang R, Chazot P, Pavese N, Crookes D, Bouridane A, Celebi ME. Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation. IEEE J Biomed Health Inform 2022; 26:2703-2713. [PMID: 35085096 DOI: 10.1109/jbhi.2022.3146369] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Facial phenotyping for medical prediagnosis has recently been successfully exploited as a novel way for the preclinical assessment of a range of rare genetic diseases, where facial biometrics is revealed to have rich links to underlying genetic or medical causes. In this paper, we aim to extend this facial prediagnosis technology for a more general dis-ease, Parkinson's Diseases (PD), and proposed an Artificial-Intelligence-of-Things (AIoT) edge-oriented privacy-preserving facial prediagnosis framework to analyze the treatment of Deep Brain Stimulation (DBS) on PD patients. In the proposed framework, a novel edge-based privacy-preserving framework is proposed to implement private deep facial diagnosis as a service over an AIoT-oriented information theoretically secure multi-party communication scheme, where partial homomorphic encryption (PHE) is leveraged to enable privacy-preserving deep facial diagnosis on encrypted facial patterns. In our experiments with a collected facial dataset from PD patients, for the first time, we proved that facial patterns could be used to evaluate the facial difference of PD patients undergoing DBS treatment. We further implemented a privacy-preserving information theoretical secure deep facial prediagnosis framework that can achieve the same accuracy as the non-encrypted one, showing the potential of our facial prediagnosis as a trust-worthy edge service for grading the severity of PD in patients.
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21
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Daly S, Hanson JT, Mavanji V, Gravely A, Jean J, Jonason A, Lewis S, Ashe J, Looft JM, McGovern RA. Using kinematics to re-define the pull test as a quantitative biomarker of the postural response in normal pressure hydrocephalus patients. Exp Brain Res 2022; 240:791-802. [PMID: 35041069 DOI: 10.1007/s00221-021-06292-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 12/14/2021] [Indexed: 11/30/2022]
Abstract
Quantitative biomarkers are needed for the diagnosis, monitoring and therapeutic assessment of postural instability in movement disorder patients. The goal of this study was to create a practical, objective measure of postural instability using kinematic measurements of the pull test. Twenty-one patients with normal pressure hydrocephalus and 20 age-matched control subjects were fitted with inertial measurement units and underwent 10-20 pull tests of varying intensities performed by a trained clinician. Kinematic data were extracted for each pull test and aggregated. Patients participated in 103 sessions for a total of 1555 trials while controls participated in 20 sessions for a total of 299 trials. Patients were separated into groups by MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) pull test score. The center of mass velocity profile easily distinguished between patient groups such that score increases correlated with decreases in peak velocity and later peak velocity onset. All patients except those scored as "3" demonstrated an increase in step length and decrease in reaction time with increasing pull intensity. Groups were distinguished by differences in the relationship of step length to pull intensity (slope) and their overall step length or reaction time regardless of pull intensity (y-intercept). NPH patients scored as "normal" on the MDS-UPDRS scale were kinematically indistinguishable from age-matched control subjects during a standardized perturbation, but could be distinguished from controls by their response to a range of pull intensities. An instrumented, purposefully varied pull test produces kinematic metrics useful for distinguishing clinically meaningful differences within hydrocephalus patients as well as distinguishing these patients from healthy, control subjects.
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Affiliation(s)
- Samuel Daly
- Department of Neurosurgery, University of Minnesota Medical School, University of Minnesota, 420 Delaware St. SE, MMC 96, Room D-429, Minneapolis, MN, 55455, USA
| | - Jacob T Hanson
- Department of Neurosurgery, University of Minnesota Medical School, University of Minnesota, 420 Delaware St. SE, MMC 96, Room D-429, Minneapolis, MN, 55455, USA
| | - Vibha Mavanji
- Division of Prosthetics, Motion Capture Analysis Laboratory, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA
| | - Amy Gravely
- Department of Statistics, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA
| | - James Jean
- Department of Neurosurgery, University of Minnesota Medical School, University of Minnesota, 420 Delaware St. SE, MMC 96, Room D-429, Minneapolis, MN, 55455, USA
| | - Alec Jonason
- Department of Neurosurgery, University of Minnesota Medical School, University of Minnesota, 420 Delaware St. SE, MMC 96, Room D-429, Minneapolis, MN, 55455, USA
| | - Scott Lewis
- Department of Neurology, University of Minnesota Medical School, Minneapolis, MN, USA.,Department of Neurology, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA
| | - James Ashe
- Department of Neurology, University of Minnesota Medical School, Minneapolis, MN, USA.,Department of Neurology, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA
| | - John M Looft
- Division of Prosthetics, Motion Capture Analysis Laboratory, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA
| | - Robert A McGovern
- Department of Neurosurgery, University of Minnesota Medical School, University of Minnesota, 420 Delaware St. SE, MMC 96, Room D-429, Minneapolis, MN, 55455, USA. .,Division of Neurosurgery, Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA.
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22
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Ahmed Z. Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2022; 190:101-125. [DOI: 10.1016/bs.pmbts.2022.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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23
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AIM in Neurodegenerative Diseases: Parkinson and Alzheimer. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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24
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Adams JL, Dinesh K, Snyder CW, Xiong M, Tarolli CG, Sharma S, Dorsey ER, Sharma G. A real-world study of wearable sensors in Parkinson's disease. NPJ Parkinsons Dis 2021; 7:106. [PMID: 34845224 PMCID: PMC8629990 DOI: 10.1038/s41531-021-00248-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 10/27/2021] [Indexed: 12/17/2022] Open
Abstract
Most wearable sensor studies in Parkinson's disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson's disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson's walked significantly less (median [inter-quartile range]: 4980 [2835-7163] steps/day) than controls (7367 [5106-8928] steps/day; P = 0.04). Tremor was present for 1.6 [0.4-5.9] hours (median [range]) per day in most-affected hands (MDS-UPDRS 3.17a or 3.17b = 1-4) of individuals with Parkinson's, which was significantly higher than the 0.5 [0.3-2.3] hours per day in less-affected hands (MDS-UPDRS 3.17a or 3.17b = 0). These results, which require replication in larger cohorts, advance our understanding of the manifestations of Parkinson's in real-world settings.
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Affiliation(s)
- Jamie L Adams
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA.
| | - Karthik Dinesh
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
| | | | - Mulin Xiong
- Michigan State University College of Human Medicine, East Lansing, MI, USA
| | - Christopher G Tarolli
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Saloni Sharma
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - E Ray Dorsey
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
- Center for Health + Technology, University of Rochester Medical Center, Rochester, NY, USA
| | - Gaurav Sharma
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA
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25
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Higgs E, Dagan-Rosenfeld O, Snyder M. Adapting skills from genetic counseling to wearables technology research during the COVID-19 pandemic: Poised for the pivot. J Genet Couns 2021; 30:1269-1275. [PMID: 34580951 DOI: 10.1002/jgc4.1509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 09/01/2021] [Accepted: 09/03/2021] [Indexed: 11/10/2022]
Abstract
Genetic counselors have shown themselves to be adaptable in an evolving profession, with expansion into new sub-specialties, various non-clinical settings, and research roles. The COVID-19 pandemic caused a sudden and drastic shift in healthcare priorities. In an effort to contribute meaningfully to the COVID-19 crisis, and to adapt to a remote- and essential-only research environment, our workplace and thus our roles pivoted from genomics research to remote COVID-19 research using wearables technologies. With a deep understanding of genomic data, we were quickly able to apply similar concepts to wearables data including considering privacy implications, managing uncertain findings, and acknowledging the lack of ethnic diversity in many datasets. By sharing our own experience as an example, we hope individuals trained in genetic counseling may see opportunities for adaptation of their skills into expanding roles.
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Affiliation(s)
- Emily Higgs
- Department of Genetics, Stanford University, Stanford, CA, USA
| | | | - Michael Snyder
- Department of Genetics, Stanford University, Stanford, CA, USA
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26
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Landers M, Saria S, Espay AJ. Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson's Disease? JOURNAL OF PARKINSONS DISEASE 2021; 11:S117-S122. [PMID: 34219671 PMCID: PMC8385515 DOI: 10.3233/jpd-212545] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The use of artificial intelligence (AI) to help diagnose and manage disease is of increasing interest to researchers and clinicians. Volumes of health data are generated from smartphones and ubiquitous inexpensive sensors. By using these data, AI can offer otherwise unobtainable insights about disease burden and patient status in a free-living environment. Moreover, from clinical datasets AI can improve patient symptom monitoring and global epidemiologic efforts. While these applications are exciting, it is necessary to examine both the utility and limitations of these novel analytic methods. The most promising uses of AI remain aspirational. For example, defining the molecular subtypes of Parkinson's disease will be assisted by future applications of AI to relevant datasets. This will allow clinicians to match patients to molecular therapies and will thus help launch precision medicine. Until AI proves its potential in pushing the frontier of precision medicine, its utility will primarily remain in individualized monitoring, complementing but not replacing movement disorders specialists.
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Affiliation(s)
- Matt Landers
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Suchi Saria
- Departments of Computer Science and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.,Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.,Bayesian Health, New York, NY, USA
| | - Alberto J Espay
- Department of Neurology, James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, USA
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27
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Javidnia M, Arbatti L, Hosamath A, Eberly SW, Oakes D, Shoulson I. Predictive Value of Verbatim Parkinson's Disease Patient-Reported Symptoms of Postural Instability and Falling. JOURNAL OF PARKINSONS DISEASE 2021; 11:1957-1964. [PMID: 34250951 PMCID: PMC8609714 DOI: 10.3233/jpd-212636] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Postural instability is an intractable sign of Parkinson’s disease, associated with poor disease prognosis, fall risk, and decreased quality of life. Objective: 1) Characterize verbatim reports of postural instability and associated symptoms (gait disorder, balance, falling, freezing, and posture), 2) compare reports with responses to three pre-specified questions from Part II of the Movement Disorder Society Unified Parkinson Disease Rating Scale (MDS-UPDRS), and 3) examine postural instability symptoms and MDS-UPDRS responses as predictors of future falls. Methods: Fox Insight research participants reported their problems attributed to PD in their own words using the Parkinson Disease Patient Reports of Problems (PD-PROP). Natural language processing, clinical curation, and data mining techniques were applied to classify text into problem domains and clinically-curated symptoms. Baseline postural instability symptoms were mapped to MDS-UPDRS questions 2.11–2.13. T-tests and chi-square tests were used to compare postural instability reporters and non-reporters, and Cochran-Armitage trend tests were used to evaluate associations between PD-PROP and MDS-UPDRS responses; survival methods were utilized to evaluate the predictive utility of PD-PROP and MDS-UPDRS responses in time-to-fall analyses. Results: Of participants within 10 years of PD diagnosis, 9,692 (56.0%) reported postural instability symptoms referable to gait unsteadiness, balance, falling, freezing, or posture at baseline. Postural instability symptoms were significantly associated with patient-reported measures from the MDS-UPDRS questions. Balance problems reported on PD-PROP and MDS-UPDRS 2.11–2.13 measures were predictive of future falls. Conclusion: Verbatim-reported problems captured by the PD-PROP and categorized by natural language processing and clinical curation and MDS-UPDRS responses predicted falls. The PD-PROP output was more granular than, and as informative as, the categorical responses.
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Affiliation(s)
- Monica Javidnia
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.,Center for Health, +, Technology, University of Rochester Medical Center, Rochester, NY, USA
| | | | | | - Shirley W Eberly
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - David Oakes
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Ira Shoulson
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.,Center for Health, +, Technology, University of Rochester Medical Center, Rochester, NY, USA.,Grey Matter Technologies Inc, Longboat Key, FL, USA
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28
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Voigt I, Inojosa H, Dillenseger A, Haase R, Akgün K, Ziemssen T. Digital Twins for Multiple Sclerosis. Front Immunol 2021; 12:669811. [PMID: 34012452 PMCID: PMC8128142 DOI: 10.3389/fimmu.2021.669811] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 04/16/2021] [Indexed: 12/16/2022] Open
Abstract
An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.
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Affiliation(s)
| | | | | | | | | | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, Technical University of Dresden, Dresden, Germany
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29
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Gannamani R, van der Veen S, van Egmond M, de Koning TJ, Tijssen MAJ. Challenges in Clinicogenetic Correlations: One Phenotype - Many Genes. Mov Disord Clin Pract 2021; 8:311-321. [PMID: 33816658 PMCID: PMC8015914 DOI: 10.1002/mdc3.13163] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 01/13/2021] [Accepted: 01/16/2021] [Indexed: 12/11/2022] Open
Abstract
Background In the field of movement disorders, what you see (phenotype) is seldom what you get (genotype). Whereas 1 phenotype was previously associated to 1 gene, the advent of next‐generation sequencing (NGS) has facilitated an exponential increase in disease‐causing genes and genotype–phenotype correlations, and the “one‐phenotype‐many‐genes” paradigm has become prominent. Objectives To highlight the “one‐phenotype‐many‐genes” paradigm by discussing the main challenges, perspectives on how to address them, and future directions. Methods We performed a scoping review of the various aspects involved in identifying the underlying molecular cause of a movement disorder phenotype. Results The notable challenges are (1) the lack of gold standards, overlap in clinical spectrum of different movement disorders, and variability in the interpretation of classification systems; (2) selecting which patients benefit from genetic tests and the choice of genetic testing; (3) problems in the variant interpretation guidelines; (4) the filtering of variants associated with disease; and (5) the lack of standardized, complete, and up‐to‐date gene lists. Perspectives to address these include (1) deep phenotyping and genotype–phenotype integration, (2) adherence to phenotype‐specific diagnostic algorithms, (3) implementation of current and complementary bioinformatic tools, (4) a clinical‐molecular diagnosis through close collaboration between clinicians and genetic laboratories, and (5) ongoing curation of gene lists and periodic reanalysis of genetic sequencing data. Conclusions Despite the rapidly emerging possibilities of NGS, there are still many steps to take to improve the genetic diagnostic yield. Future directions, including post‐NGS phenotyping and cohort analyses enriched by genotype–phenotype integration and gene networks, ought to be pursued to accelerate identification of disease‐causing genes and further improve our understanding of disease biology.
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Affiliation(s)
- Rahul Gannamani
- Department of Neurology University of Groningen, University Medical Centre Groningen Groningen The Netherlands.,Department of Genetics University of Groningen, University Medical Centre Groningen Groningen The Netherlands.,Expertise Centre Movement Disorders Groningen University Medical Centre Groningen Groningen The Netherlands
| | - Sterre van der Veen
- Department of Neurology University of Groningen, University Medical Centre Groningen Groningen The Netherlands.,Expertise Centre Movement Disorders Groningen University Medical Centre Groningen Groningen The Netherlands
| | - Martje van Egmond
- Department of Neurology University of Groningen, University Medical Centre Groningen Groningen The Netherlands.,Expertise Centre Movement Disorders Groningen University Medical Centre Groningen Groningen The Netherlands
| | - Tom J de Koning
- Department of Genetics University of Groningen, University Medical Centre Groningen Groningen The Netherlands.,Expertise Centre Movement Disorders Groningen University Medical Centre Groningen Groningen The Netherlands.,Pediatrics, Department of Clinical Sciences Lund University Lund Sweden
| | - Marina A J Tijssen
- Department of Neurology University of Groningen, University Medical Centre Groningen Groningen The Netherlands.,Expertise Centre Movement Disorders Groningen University Medical Centre Groningen Groningen The Netherlands
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30
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Tan AH, Hor JW, Chong CW, Lim S. Probiotics for Parkinson's disease: Current evidence and future directions. JGH Open 2021; 5:414-419. [PMID: 33860090 PMCID: PMC8035463 DOI: 10.1002/jgh3.12450] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 10/27/2020] [Indexed: 12/12/2022]
Abstract
The gut-brain axis is a hot topic in Parkinson's disease (PD). It has been postulated that gut pathogens and dysbiosis can contribute to peripheral inflammatory states or trigger downstream metabolic effects that exacerbate the neurodegenerative process in PD. Several preclinical and clinical studies have demonstrated disrupted intestinal permeability, intestinal inflammation, altered gut microbiome, and reduced fecal short-chain fatty acids in PD. In this regard, microbial-directed therapies such as probiotics are emerging as potential therapeutic options. Probiotic supplementation is postulated to confer a variety of health benefits due to the diverse functions of these live microorganisms, including inhibition of pathogen colonization, modulation/"normalization" of the microbiome and/or its function, immunomodulatory effects (e.g. reducing inflammation), and improved host epithelial barrier function. Interestingly, several PD animal model studies have demonstrated the potential neuroprotective effects of probiotics in reducing dopaminergic neuronal degeneration. Notably, two randomized placebo-controlled trials have provided class I evidence for probiotics as a treatment for constipation in PD. However, the effects of probiotics on other PD aspects, such as motor disability and cognitive function, and its long-term efficacy (including effects on PD drug absorption in the gut) have not been investigated adequately. Further targeted animal and human studies are also warranted to understand the mechanisms of actions of probiotics in PD and to tailor probiotic therapy based on individual host profiles to improve patient outcomes in this disabling disorder.
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Affiliation(s)
- Ai Huey Tan
- Division of Neurology and the Mah Pooi Soo and Tan Chin Nam Centre for Parkinson's and Related Disorders, Faculty of MedicineUniversity of MalayaKuala LumpurMalaysia
| | - Jia Wei Hor
- Division of Neurology and the Mah Pooi Soo and Tan Chin Nam Centre for Parkinson's and Related Disorders, Faculty of MedicineUniversity of MalayaKuala LumpurMalaysia
| | - Chun Wie Chong
- School of PharmacyMonash University MalaysiaSelangorMalaysia
| | - Shen‐Yang Lim
- Division of Neurology and the Mah Pooi Soo and Tan Chin Nam Centre for Parkinson's and Related Disorders, Faculty of MedicineUniversity of MalayaKuala LumpurMalaysia
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31
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Digital Technology in Movement Disorders: Updates, Applications, and Challenges. Curr Neurol Neurosci Rep 2021; 21:16. [PMID: 33660110 PMCID: PMC7928701 DOI: 10.1007/s11910-021-01101-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/21/2021] [Indexed: 12/14/2022]
Abstract
Purpose of Review Digital technology affords the opportunity to provide objective, frequent, and sensitive assessment of disease outside of the clinic environment. This article reviews recent literature on the application of digital technology in movement disorders, with a focus on Parkinson’s disease (PD) and Huntington’s disease. Recent Findings Recent research has demonstrated the ability for digital technology to discriminate between individuals with and without PD, identify those at high risk for PD, quantify specific motor features, predict clinical events in PD, inform clinical management, and generate novel insights. Summary Digital technology has enormous potential to transform clinical research and care in movement disorders. However, more work is needed to better validate existing digital measures, including in new populations, and to develop new more holistic digital measures that move beyond motor features.
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32
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Schneider RB, Omberg L, Macklin EA, Daeschler M, Bataille L, Anthwal S, Myers TL, Baloga E, Duquette S, Snyder P, Amodeo K, Tarolli CG, Adams JL, Callahan KF, Gottesman J, Kopil CM, Lungu C, Ascherio A, Beck JC, Biglan K, Espay AJ, Tanner C, Oakes D, Shoulson I, Novak D, Kayson E, Ray Dorsey E, Mangravite L, Schwarzschild MA, Simuni T. Design of a virtual longitudinal observational study in Parkinson's disease (AT-HOME PD). Ann Clin Transl Neurol 2021; 8:308-320. [PMID: 33350601 PMCID: PMC7886038 DOI: 10.1002/acn3.51236] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 10/11/2020] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE The expanding power and accessibility of personal technology provide an opportunity to reduce burdens and costs of traditional clinical site-centric therapeutic trials in Parkinson's disease and generate novel insights. The value of this approach has never been more evident than during the current COVID-19 pandemic. We sought to (1) establish and implement the infrastructure for longitudinal, virtual follow-up of clinical trial participants, (2) compare changes in smartphone-based assessments, online patient-reported outcomes, and remote expert assessments, and (3) explore novel digital markers of Parkinson's disease disability and progression. METHODS Participants from two recently completed phase III clinical trials of inosine and isradipine enrolled in Assessing Tele-Health Outcomes in Multiyear Extensions of Parkinson's Disease trials (AT-HOME PD), a two-year virtual cohort study. After providing electronic informed consent, individuals complete annual video visits with a movement disorder specialist, smartphone-based assessments of motor function and socialization, and patient-reported outcomes online. RESULTS From the two clinical trials, 226 individuals from 42 states in the United States and Canada enrolled. Of these, 181 (80%) have successfully downloaded the study's smartphone application and 161 (71%) have completed patient-reported outcomes on the online platform. INTERPRETATION It is feasible to conduct a large-scale, international virtual observational study following the completion of participation in brick-and-mortar clinical trials in Parkinson's disease. This study, which brings research to participants, will compare established clinical endpoints with novel digital biomarkers and thereby inform the longitudinal follow-up of clinical trial participants and design of future clinical trials.
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Affiliation(s)
- Ruth B. Schneider
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | | | - Eric A. Macklin
- Biostatistics CenterMassachusetts General HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Margaret Daeschler
- The Michael J. Fox Foundation for Parkinson’s ResearchNew YorkNew YorkUSA
| | - Lauren Bataille
- The Michael J. Fox Foundation for Parkinson’s ResearchNew YorkNew YorkUSA
| | - Shalini Anthwal
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Taylor L. Myers
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Elizabeth Baloga
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Sidney Duquette
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | | | - Katherine Amodeo
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Christopher G Tarolli
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Jamie L. Adams
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | | | - Joshua Gottesman
- The Michael J. Fox Foundation for Parkinson’s ResearchNew YorkNew YorkUSA
| | - Catherine M. Kopil
- The Michael J. Fox Foundation for Parkinson’s ResearchNew YorkNew YorkUSA
| | - Codrin Lungu
- Division of Clinical ResearchNational Institute of Neurological Disorders and StrokeBethesdaMarylandUSA
| | - Alberto Ascherio
- Department of NutritionHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | | | - Kevin Biglan
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Eli Lilly and CompanyIndianapolisIndianaUSA
| | - Alberto J. Espay
- Department of NeurologyUniversity of CincinnatiCincinnatiOhioUSA
| | - Caroline Tanner
- Department of NeurologyWeill Institute for NeurosciencesUniversity of CaliforniaSan Francisco Veterans Affairs Health Care SystemSan FranciscoCaliforniaUSA
| | - David Oakes
- Department of BiostatisticsUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Ira Shoulson
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Grey Matter TechnologiesSarasotaFloridaUSA
| | - Dan Novak
- Parkinson’s FoundationNew YorkNew YorkUSA
| | - Elise Kayson
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Earl Ray Dorsey
- Department of NeurologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
- Center for Health + TechnologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | | | | | - Tanya Simuni
- Department of NeurologyNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
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33
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Davids J, Ashrafian H. AIM in Neurodegenerative Diseases: Parkinson and Alzheimer. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_190-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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