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Bhidayasiri R. Old problems, new solutions: harnessing technology and innovation in Parkinson's disease-evidence and experiences from Thailand. J Neural Transm (Vienna) 2024; 131:721-738. [PMID: 38189972 DOI: 10.1007/s00702-023-02727-1] [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: 11/06/2023] [Accepted: 12/09/2023] [Indexed: 01/09/2024]
Abstract
The prevalence of Parkinson's disease (PD) is increasing rapidly worldwide, but there are notable inequalities in its distribution and in the availability of healthcare resources across different world regions. Low- and middle-income countries (LMICs), including Thailand, bear the highest burden of PD so there is an urgent need to develop effective solutions that can overcome the many regional challenges associated with delivering high-quality, and equitable care to a diverse population with limited resources. This article describes the evolution of healthcare delivery for PD in Thailand, as a case example of a LMIC. The discussions reflect the author's presentation at the Yoshikuni Mizuno Lectureship Award given during the 8th Asian and Oceanian Parkinson's Disease and Movement Disorders Congress in March 2023 for which he was the 2023 recipient. The specific challenges faced in Thailand are reviewed along with new solutions that have been implemented to improve the knowledge and skills of healthcare professionals nationally, the delivery of care, and the outcomes for PD patients. Technology and innovation have played an important role in this process with many new tools and devices being implemented in clinical practice. Without any realistic prospect of a curative therapy in the near future that could halt the current PD pandemic, it will be necessary to focus on preventative lifestyle strategies that can help reduce the risk of developing PD such as good nutrition (EAT), exercise (MOVE), good sleep hygiene (SLEEP), and minimizing environmental risks (PROTECT), which should be initiated and continued (REPEAT) as early as possible.
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Affiliation(s)
- Roongroj Bhidayasiri
- Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama 4 Road, Bangkok, 10330, Thailand.
- The Academy of Science, The Royal Society of Thailand, Bangkok, 10330, Thailand.
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Bhidayasiri R, Sringean J, Phumphid S, Anan C, Thanawattano C, Deoisres S, Panyakaew P, Phokaewvarangkul O, Maytharakcheep S, Buranasrikul V, Prasertpan T, Khontong R, Jagota P, Chaisongkram A, Jankate W, Meesri J, Chantadunga A, Rattanajun P, Sutaphan P, Jitpugdee W, Chokpatcharavate M, Avihingsanon Y, Sittipunt C, Sittitrai W, Boonrach G, Phonsrithong A, Suvanprakorn P, Vichitcholchai J, Bunnag T. The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand. Front Neurol 2024; 15:1386608. [PMID: 38803644 PMCID: PMC11129688 DOI: 10.3389/fneur.2024.1386608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 04/19/2024] [Indexed: 05/29/2024] Open
Abstract
The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.
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Affiliation(s)
- Roongroj Bhidayasiri
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- The Academy of Science, The Royal Society of Thailand, Bangkok, Thailand
| | - Jirada Sringean
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Saisamorn Phumphid
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Chanawat Anan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | | | - Suwijak Deoisres
- National Electronics and Computer Technology Centre, Pathum Thani, Thailand
| | - Pattamon Panyakaew
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Onanong Phokaewvarangkul
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Suppata Maytharakcheep
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Vijittra Buranasrikul
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Tittaya Prasertpan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- Sawanpracharak Hospital, Nakhon Sawan, Thailand
| | | | - Priya Jagota
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Araya Chaisongkram
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Worawit Jankate
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Jeeranun Meesri
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Araya Chantadunga
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Piyaporn Rattanajun
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Phantakarn Sutaphan
- Chulalongkorn Centre of Excellence for Parkinson’s Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Weerachai Jitpugdee
- Department of Rehabilitation Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Marisa Chokpatcharavate
- Chulalongkorn Parkinson's Disease Support Group, Department of Medicine, Faculty of Medicine, Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Yingyos Avihingsanon
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Society, Bangkok, Thailand
| | - Chanchai Sittipunt
- Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
- Thai Red Cross Society, Bangkok, Thailand
| | | | | | | | | | | | - Tej Bunnag
- Thai Red Cross Society, Bangkok, Thailand
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Lee J, Suh Y, Kim E, Yoo S, Kim Y. A Mobile App for Comprehensive Symptom Management in People With Parkinson's Disease: A Pilot Usability Study. Comput Inform Nurs 2024; 42:289-297. [PMID: 38261451 DOI: 10.1097/cin.0000000000001089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
There is an increasing need for highly accessible health management platforms for comprehensive symptoms of Parkinson disease. Mobile apps encompassing nonmotor symptoms have been rarely developed since these symptoms are often subjective and difficult to reflect what individuals actually experience. The study developed an app for comprehensive symptom management and evaluated its usability and feasibility. A single-group repeated measurement experimental design was used. Twenty-two participants used the app for 6 weeks. Monitoring of nonmotor symptoms, games to address motor symptoms, and medication management were incorporated in the app. Quantitative outcomes were self-assessed through an online questionnaire, and one-on-one telephone interviews were conducted to understand the user's point of view. The successful experience of self-monitoring had improved participants' self-efficacy ( Z = -3.634, P < .001) and medication adherence ( Z = -3.371, P = .001). Facilitators included a simple-to-use interface, entertaining content, and medication helps. Barriers included simple forgetfulness and digital literacy, including unfamiliarity with mobile phone manipulation itself. The study suggested insight into the app use related to acceptability of mobile technology. The preliminary effects on self-efficacy and medication adherence will guide future nursing interventions using mobile health. Our approach will contribute to improving the continuum of care for Parkinson disease by promoting self-monitoring of symptoms.
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Affiliation(s)
- JuHee Lee
- Author Affiliations: Mo-Im Kim Nursing Research Institute, Yonsei Evidence Based Nursing Centre of Korea: A Joanna Briggs Institute of Excellence, College of Nursing, Yonsei University (Dr Lee), Seoul; College of Nursing, Health Science & Human Ecology, Dong-Eui University (Dr Suh), Busan; and Graduate School, Brain Korea 21 FOUR Project, College of Nursing, Yonsei University (Mss E. Kim and Yoo); and Division of Nursing, Severance Hospital, Yonsei University Health System (Dr Y. Kim), Seoul, South Korea
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Park KW, Mirian MS, McKeown MJ. Artificial intelligence-based video monitoring of movement disorders in the elderly: a review on current and future landscapes. Singapore Med J 2024; 65:141-149. [PMID: 38527298 PMCID: PMC11060643 DOI: 10.4103/singaporemedj.smj-2023-189] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/19/2023] [Indexed: 03/27/2024]
Abstract
ABSTRACT Due to global ageing, the burden of chronic movement and neurological disorders (Parkinson's disease and essential tremor) is rapidly increasing. Current diagnosis and monitoring of these disorders rely largely on face-to-face assessments utilising clinical rating scales, which are semi-subjective and time-consuming. To address these challenges, the utilisation of artificial intelligence (AI) has emerged. This review explores the advantages and challenges associated with using AI-driven video monitoring to care for elderly patients with movement disorders. The AI-based video monitoring systems offer improved efficiency and objectivity in remote patient monitoring, enabling real-time analysis of data, more uniform outcomes and augmented support for clinical trials. However, challenges, such as video quality, privacy compliance and noisy training labels, during development need to be addressed. Ultimately, the advancement of video monitoring for movement disorders is expected to evolve towards discreet, home-based evaluations during routine daily activities. This progression must incorporate data security, ethical considerations and adherence to regulatory standards.
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Affiliation(s)
- Kye Won Park
- Pacific Parkinson Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Maryam S Mirian
- Pacific Parkinson Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Martin J McKeown
- Pacific Parkinson Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
- Department of Medicine (Neurology), University of British Columbia, Vancouver, British Columbia, Canada
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Willemse IHJ, Schootemeijer S, van den Bergh R, Dawes H, Nonnekes JH, van de Warrenburg BPC. Smartphone applications for Movement Disorders: Towards collaboration and re-use. Parkinsonism Relat Disord 2024; 120:105988. [PMID: 38184466 DOI: 10.1016/j.parkreldis.2023.105988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/20/2023] [Accepted: 12/31/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Numerous smartphone and tablet applications (apps) are available to monitor movement disorders, but an overview of their purpose and stage of development is missing. OBJECTIVES To systematically review published literature and classify smartphone and tablet apps with objective measurement capabilities for the diagnosis, monitoring, assessment, or treatment of movement disorders. METHODS We systematically searched for publications covering smartphone or tablet apps to monitor movement disorders until November 22nd, 2023. We reviewed the target population, measured domains, purpose, and technology readiness level (TRL) of the proposed app and checked their availability in common app stores. RESULTS We identified 113 apps. Most apps were developed for Parkinson's disease specifically (n = 82; 73%) or for movement disorders in general (n = 17; 15%). Apps were either designed to momentarily assess symptoms (n = 65; 58%), support treatment (n = 22; 19%), aid in diagnosis (n = 16; 14%), or passively track symptoms (n = 11; 10%). Commonly assessed domains across movement disorders included fine motor skills (n = 34; 30%), gait (n = 36; 32%), and tremor (n = 32; 28%) for the motor domain and cognition (n = 16; 14%) for the non-motor domain. Twenty-six (23%) apps were proof-of-concepts (TRL 1-3), while most apps were tested in a controlled setting (TRL 4-6; n = 63; 56%). Twenty-four apps were tested in their target setting (TRL 7-9) of which 10 were accessible in common app stores or as Android Package. CONCLUSIONS The development of apps strongly gravitates towards Parkinson's disease and a selection of motor symptoms. Collaboration, re-use and further development of existing apps is encouraged to avoid reinventions of the wheel.
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Affiliation(s)
- Ilse H J Willemse
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands.
| | - Sabine Schootemeijer
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Robin van den Bergh
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Helen Dawes
- NIHR Exeter BRC, Medical School, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Jorik H Nonnekes
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Rehabilitation, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands; Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, the Netherlands
| | - Bart P C van de Warrenburg
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
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Lizarraga KJ, Gyang T, Benson RT, Birbeck GL, Johnston KC, Royal W, Sacco RL, Segal B, Vickrey BG, Griggs RC, Holloway RG. Seven Strategies to Integrate Equity within Translational Research in Neurology. Ann Neurol 2024; 95:432-441. [PMID: 38270253 PMCID: PMC10922988 DOI: 10.1002/ana.26873] [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] [Received: 06/30/2023] [Revised: 12/27/2023] [Accepted: 12/28/2023] [Indexed: 01/26/2024]
Abstract
The rapidly accelerating translation of biomedical advances is leading to revolutionary therapies that are often inaccessible to historically marginalized populations. We identified and synthesized recent guidelines and statements to propose 7 strategies to integrate equity within translational research in neurology: (1) learn history; (2) learn about upstream forces; (3) diversify and liberate; (4) change narratives and adopt best communication practices; (5) study social drivers of health and lived experiences; (6) leverage health technologies; and (7) build, sustain, and lead culturally humble teams. We propose that equity should be a major goal of translational research, equally important as safety and efficacy. ANN NEUROL 2024;95:432-441.
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Affiliation(s)
| | - Tirisham Gyang
- Department of Neurology, The Ohio State University, Columbus, OH, USA
| | - Richard T. Benson
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | | | - Karen C. Johnston
- Department of Neurology, University of Virginia, Charlottesville, VA, USA
| | - Walter Royal
- Department of Neurobiology and Neuroscience Institute, Morehouse School of Medicine, Atlanta, GA, USA
| | - Ralph L. Sacco
- Department of Neurology, University of Miami, Miami, FL, USA
| | - Benjamin Segal
- Department of Neurology, The Ohio State University, Columbus, OH, USA
| | - Barbara G. Vickrey
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert C. Griggs
- Department of Neurology, University of Rochester, Rochester, NY, USA
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Salaorni F, Bonardi G, Schena F, Tinazzi M, Gandolfi M. Wearable devices for gait and posture monitoring via telemedicine in people with movement disorders and multiple sclerosis: a systematic review. Expert Rev Med Devices 2024; 21:121-140. [PMID: 38124300 DOI: 10.1080/17434440.2023.2298342] [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: 03/15/2023] [Accepted: 12/19/2023] [Indexed: 12/23/2023]
Abstract
INTRODUCTION Wearable devices and telemedicine are increasingly used to track health-related parameters across patient populations. Since gait and postural control deficits contribute to mobility deficits in persons with movement disorders and multiple sclerosis, we thought it interesting to evaluate devices in telemedicine for gait and posture monitoring in such patients. METHODS For this systematic review, we searched the electronic databases MEDLINE (PubMed), SCOPUS, Cochrane Library, and SPORTDiscus. Of the 452 records retrieved, 12 met the inclusion/exclusion criteria. Data about (1) study characteristics and clinical aspects, (2) technical, and (3) telemonitoring and teleconsulting were retrieved, The studies were quality assessed. RESULTS All studies involved patients with Parkinson's disease; most used triaxial accelerometers for general assessment (n = 4), assessment of motor fluctuation (n = 3), falls (n = 2), and turning (n = 3). Sensor placement and count varied widely across studies. Nine used lab-validated algorithms for data analysis. Only one discussed synchronous patient feedback and asynchronous teleconsultation. CONCLUSIONS Wearable devices enable real-world patient monitoring and suggest biomarkers for symptoms and behaviors related to underlying gait disorders. thus enriching clinical assessment and personalized treatment plans. As digital healthcare evolves, further research is needed to enhance device accuracy, assess user acceptability, and integrate these tools into telemedicine infrastructure. PROSPERO REGISTRATION CRD42022355460.
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Affiliation(s)
- Francesca Salaorni
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Giulia Bonardi
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Federico Schena
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Michele Tinazzi
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Marialuisa Gandolfi
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
- Neuromotor and Cognitive Rehabilitation Research Centre (CRRNC), University of Verona, Verona, Italy
- Neurorehabilitation Unit - Azienda Ospedaliera Universitaria Integrata, Verona
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Tsamis KI, Odin P, Antonini A, Reichmann H, Konitsiotis S. A Paradigm Shift in the Management of Patients with Parkinson's Disease. NEURODEGENER DIS 2023; 23:13-19. [PMID: 37913759 PMCID: PMC10659004 DOI: 10.1159/000533798] [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: 04/08/2023] [Accepted: 08/23/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND Technological evolution leads to the constant enhancement of monitoring systems and recording symptoms of diverse disorders. SUMMARY For Parkinson's disease, wearable devices empowered with machine learning analysis are the main modules for objective measurements. Software and hardware improvements have led to the development of reliable systems that can detect symptoms accurately and be implicated in the follow-up and treatment decisions. KEY MESSAGES Among many different devices developed so far, the most promising ones are those that can record symptoms from all extremities and the trunk, in the home environment during the activities of daily living, assess gait impairment accurately, and be suitable for a long-term follow-up of the patients. Such wearable systems pave the way for a paradigm shift in the management of patients with Parkinson's disease.
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Affiliation(s)
- Konstantinos I. Tsamis
- Department of Physiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
- Department of Neurology, University Hospital of Ioannina, University of Ioannina, Ioannina, Greece
| | - Per Odin
- Division of Neurology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Angelo Antonini
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration CESNE, Department of Neuroscience, University of Padova, Padova, Italy
| | - Heinz Reichmann
- Department of Neurology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Spyridon Konitsiotis
- Department of Neurology, University Hospital of Ioannina, University of Ioannina, Ioannina, Greece
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Fowler King B, MacDonald J, Stoff L, Nettnin E, Jayaraman A, Goldman JG, Rafferty M. Activity Monitoring in Parkinson Disease: A Qualitative Study of Implementation Determinants. J Neurol Phys Ther 2023; 47:189-199. [PMID: 37306418 DOI: 10.1097/npt.0000000000000451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND PURPOSE There is interest in incorporating digital health technology in routine practice. We integrate multiple stakeholder perspectives to describe implementation determinants (barriers and facilitators) regarding digital health technology use to facilitate exercise behavior change for people with Parkinson disease in outpatient physical therapy. METHODS The purposeful sample included people with Parkinson disease (n = 13), outpatient physical therapists (n = 12), and advanced technology stakeholders including researchers and reimbursement specialists (n = 13). Semistructured interviews were used to elicit implementation determinants related to using digital health technology for activity monitoring and exercise behavior change. Deductive codes based on the Consolidated Framework for Implementation Research were used to describe implementation determinants. RESULTS Key implementation determinants were similar across stakeholder groups. Essential characteristics of digital health technology included design quality and packaging, adaptability, complexity, and cost. Implementation of digital health technology by physical therapists and people with Parkinson disease was influenced by their knowledge, attitudes, and varied confidence levels in using digital health technology. Inner setting organizational determinants included available resources and access to knowledge/information. Process determinants included device interoperability with medical record systems and workflow integration. Outer setting barriers included lack of external policies, regulations, and collaboration with device companies. DISCUSSION AND CONCLUSIONS Future implementation interventions should address key determinants, including required processes for how and when physical therapists instruct people with Parkinson disease on digital health technology, organizational readiness, workflow integration, and characteristics of physical therapists and people with Parkinson disease who may have ingrained beliefs regarding their ability and willingness to use digital health technology. Although site-specific barriers should be addressed, digital health technology knowledge translation tools tailored to individuals with varied confidence levels may be generalizable across clinics.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content available at: http://links.lww.com/JNPT/A436 ).
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Affiliation(s)
- Bridget Fowler King
- Shirley Ryan AbilityLab, Chicago, Illinois (B.F.K., J.M., L.S., E.N., A.J., J.G.G., M.R.); and Departments of Physical Medicine and Rehabilitation (A.J., J.G.G., M.R.), Physical Therapy & Human Movement Sciences (A.J.), Medical Social Sciences (A.J.), Neurology (J.G.G), and Psychiatry and Behavioral Science (M.R.), Northwestern University Feinberg School of Medicine, Chicago, Illinois
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10
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Bajenaru L, Sorici A, Mocanu IG, Florea AM, Antochi FA, Ribigan AC. Shared Decision-Making to Improve Health-Related Outcomes for Adults with Stroke Disease. Healthcare (Basel) 2023; 11:1803. [PMID: 37372920 DOI: 10.3390/healthcare11121803] [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: 05/11/2023] [Revised: 06/02/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
Stroke is one of the leading causes of disability and death worldwide, a severe medical condition for which new solutions for prevention, monitoring, and adequate treatment are needed. This paper proposes a SDM framework for the development of innovative and effective solutions based on artificial intelligence in the rehabilitation of stroke patients by empowering patients to make decisions about the use of devices and applications developed in the European project ALAMEDA. To develop a predictive tool for improving disability in stroke patients, key aspects of stroke patient data collection journeys, monitored health parameters, and specific variables covering motor, physical, emotional, cognitive, and sleep status are presented. The proposed SDM model involved the training and consultation of patients, medical staff, carers, and representatives under the name of the Local Community Group. Consultation with LCG members, consists of 11 representative people, physicians, nurses, patients and caregivers, which led to the definition of a methodological framework to investigate the key aspects of monitoring the patient data collection journey for the stroke pilot, and a specific questionnaire to collect stroke patient requirements and preferences. A set of general and specific guidelines specifying the principles by which patients decide to use wearable sensing devices and specific applications resulted from the analysis of the data collected using the questionnaire. The preferences and recommendations collected from LCG members have already been implemented in this stage of ALAMEDA system design and development.
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Affiliation(s)
- Lidia Bajenaru
- Department of Computer Science, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
| | - Alexandru Sorici
- Department of Computer Science, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
| | - Irina Georgiana Mocanu
- Department of Computer Science, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
| | - Adina Magda Florea
- Department of Computer Science, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania
| | - Florina Anca Antochi
- Department of Neurology, University Emergency Hospital Bucharest, 169 Splaiul Independentei, 050098 Bucharest, Romania
| | - Athena Cristina Ribigan
- Department of Neurology, University Emergency Hospital Bucharest, 169 Splaiul Independentei, 050098 Bucharest, Romania
- Department of Neurology, Faculty of Medicine, University of Medicine and Pharmacy "Carol Davila" Bucharest, 37 Dionisie Lupu, 020021 Bucharest, Romania
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Chahine LM, Simuni T. Role of novel endpoints and evaluations of response in Parkinson disease. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:325-345. [PMID: 36803820 DOI: 10.1016/b978-0-323-85555-6.00010-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
With progress in our understanding of Parkinson disease (PD) and other neurodegenerative disorders, from clinical features to imaging, genetic, and molecular characterization comes the opportunity to refine and revise how we measure these diseases and what outcome measures are used as endpoints in clinical trials. While several rater-, patient-, and milestone-based outcomes for PD exist that may serve as clinical trial endpoints, there remains an unmet need for endpoints that are clinically meaningful, patient centric while also being more objective and quantitative, less susceptible to effects of symptomatic therapy (for disease-modification trials), and that can be measured over a short period and yet accurately represent longer-term outcomes. Several novel outcomes that may be used as endpoints in PD clinical trials are in development, including digital measures of signs and symptoms, as well a growing array of imaging and biospecimen biomarkers. This chapter provides an overview of the state of PD outcome measures as of 2022, including considerations for selection of clinical trial endpoints in PD, advantages and limitations of existing measures, and emerging potential novel endpoints.
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Affiliation(s)
- Lana M Chahine
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Tanya Simuni
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
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12
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Wrist Rigidity Evaluation in Parkinson’s Disease: A Scoping Review. Healthcare (Basel) 2022; 10:healthcare10112178. [DOI: 10.3390/healthcare10112178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 10/21/2022] [Accepted: 10/26/2022] [Indexed: 11/04/2022] Open
Abstract
(1) Background: One of the main cardinal signs of Parkinson’s disease (PD) is rigidity, whose assessment is important for monitoring the patient’s recovery. The wrist is one of the joints most affected by this symptom, which has a great impact on activities of daily living and consequently on quality of life. The assessment of rigidity is traditionally made by clinical scales, which have limitations due to their subjectivity and low intra- and inter-examiner reliability. (2) Objectives: To compile the main methods used to assess wrist rigidity in PD and to study their validity and reliability, a scope review was conducted. (3) Methods: PubMed, IEEE/IET Electronic Library, Web of Science, Scopus, Cochrane, Bireme, Google Scholar and Science Direct databases were used. (4) Results: Twenty-eight studies were included. The studies presented several methods for quantitative assessment of rigidity using instruments such as force and inertial sensors. (5) Conclusions: Such methods present good correlation with clinical scales and are useful for detecting and monitoring rigidity. However, the development of a standard quantitative method for assessing rigidity in clinical practice remains a challenge.
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Arpan I, Shah VV, McNames J, Harker G, Carlson-Kuhta P, Spain R, El-Gohary M, Mancini M, Horak FB. Fall Prediction Based on Instrumented Measures of Gait and Turning in Daily Life in People with Multiple Sclerosis. SENSORS (BASEL, SWITZERLAND) 2022; 22:5940. [PMID: 36015700 PMCID: PMC9415310 DOI: 10.3390/s22165940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 08/02/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
This study investigates the potential of passive monitoring of gait and turning in daily life in people with multiple sclerosis (PwMS) to identify those at future risk of falls. Seven days of passive monitoring of gait and turning were carried out in a pilot study of 26 PwMS in home settings using wearable inertial sensors. The retrospective fall history was collected at the baseline. After gait and turning data collection in daily life, PwMS were followed biweekly for a year and were classified as fallers if they experienced >1 fall. The ability of short-term passive monitoring of gait and turning, as well as retrospective fall history to predict future falls were compared using receiver operator curves and regression analysis. The history of retrospective falls was not identified as a significant predictor of future falls in this cohort (AUC = 0.62, p = 0.32). Among quantitative monitoring measures of gait and turning, the pitch at toe-off was the best predictor of falls (AUC = 0.86, p < 0.01). Fallers had a smaller pitch of their feet at toe-off, reflecting less plantarflexion during the push-off phase of walking, which can impact forward propulsion and swing initiation and can result in poor foot clearance and an increased metabolic cost of walking. In conclusion, our cohort of PwMS showed that objective monitoring of gait and turning in daily life can identify those at future risk of falls, and the pitch at toe-off was the single most influential predictor of future falls. Therefore, interventions aimed at improving the strength of plantarflexion muscles, range of motion, and increased proprioceptive input may benefit PwMS at future fall risk.
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Affiliation(s)
- Ishu Arpan
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
- Advanced Imaging Research Center, Oregon Health & Science University Portland, OR 97239, USA
| | - Vrutangkumar V. Shah
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
- APDM Wearable Technologies-A Clario Company, 2828 S Corbett Ave, Ste 135, Portland, OR 97201, USA
| | - James McNames
- APDM Wearable Technologies-A Clario Company, 2828 S Corbett Ave, Ste 135, Portland, OR 97201, USA
- Department of Electrical and Computer Engineering, Portland State University, 1825 SW Broadway, Portland, OR 97201, USA
| | - Graham Harker
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
| | | | - Rebecca Spain
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Mahmoud El-Gohary
- APDM Wearable Technologies-A Clario Company, 2828 S Corbett Ave, Ste 135, Portland, OR 97201, USA
| | - Martina Mancini
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
| | - Fay B. Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR 97239, USA
- APDM Wearable Technologies-A Clario Company, 2828 S Corbett Ave, Ste 135, Portland, OR 97201, USA
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Hoang TH, Zehni M, Xu H, Heintz G, Zallek C, Do MN. Towards a Comprehensive Solution for a Vision-based Digitized Neurological Examination. IEEE J Biomed Health Inform 2022; 26:4020-4031. [PMID: 35439148 DOI: 10.1109/jbhi.2022.3167927] [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: 11/07/2022]
Abstract
The ability to use digitally recorded and quantified neurological exam information is important to help healthcare systems deliver better care, in-person and via telehealth, as they compensate for a growing shortage of neurologists. Current neurological digital biomarker pipelines, however, are narrowed down to a specific neurological exam component or applied for assessing specific conditions. In this paper, we propose an accessible vision-based exam and documentation solution called Digitized Neurological Examination (DNE) to expand exam biomarker recording options and clinical applications using a smartphone/tablet. Through our DNE software, healthcare providers in clinical settings and people at home are enabled to video capture an examination while performing instructed neurological tests, including finger tapping, finger to finger, forearm roll, and stand-up and walk. Our modular design of the DNE software supports integrations of additional tests. The DNE extracts from the recorded examinations the 2D/3D human-body pose and quantifies kinematic and spatio-temporal features. The features are clinically relevant and allow clinicians to document and observe the quantified movements and the changes of these metrics over time. A web server and a user interface for recordings viewing and feature visualizations are available. DNE was evaluated on a collected dataset of 21 subjects containing normal and simulated-impaired movements. The overall accuracy of DNE is demonstrated by classifying the recorded movements using various machine learning models. Our tests show an accuracy beyond 90% for upper-limb tests and 80% for the stand-up and walk tests.
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Erickson ML, Wang W, Counts J, Redman LM, Parker D, Huebner JL, Dunn J, Kraus WE. Field-Based Assessments of Behavioral Patterns During Shiftwork in Police Academy Trainees Using Wearable Technology. J Biol Rhythms 2022; 37:260-271. [PMID: 35416084 DOI: 10.1177/07487304221087068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Circadian misalignment, as occurs in shiftwork, is associated with numerous negative health outcomes. Here, we sought to improve data labeling accuracy from wearable technology using a novel data pre-processing algorithm in 27 police trainees during shiftwork. Secondarily, we explored changes in four metabolic salivary biomarkers of circadian rhythm during shiftwork. Using a two-group observational study design, participants completed in-class training during dayshift for 6 weeks followed by either dayshift or nightshift field-training for 6 weeks. Using our novel algorithm, we imputed labels of circadian misaligned sleep episodes that occurred during daytime, which were previously were mislabeled as non-sleep by Garmin, supported by algorithm performance analysis. We next assessed changes to resting heart rate and sleep regularity index during dayshift versus nightshift field-training. We also examined changes in field-based assessments of salivary cortisol, uric acid, testosterone, and melatonin during dayshift versus nightshift. Compared to dayshift, nightshift workers experienced larger changes to resting heart rate, sleep regularity index (indicating reduced sleep regularity), and alterations in sleep/wake activity patterns accompanied by blunted salivary cortisol. Salivary uric acid and testosterone did not change. These findings show wearable technology combined with specialized data pre-processing can be used to monitor changes in behavioral patterns during shiftwork.
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Affiliation(s)
| | - Will Wang
- Department of Biomedical Engineering, Duke University, Durham, North Carolina
| | - Julie Counts
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina
| | - Leanne M Redman
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana
| | - Daniel Parker
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina
| | - Janet L Huebner
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, North Carolina.,Department of Biostatistics & Bioinformatics, Duke University, Durham, North Carolina
| | - William E Kraus
- Duke Molecular Physiology Institute, Duke University, Durham, North Carolina.,Department of Biomedical Engineering, Duke University, Durham, North Carolina
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Zumaeta K, Romero SE, Torres E, Urdiales L, Ramirez A, Camargo I, Lizarraga KJ, Castaneda B. Combining inertial sensors and optical flow to assess finger movements: Pilot study for telehealth applications. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2409-2412. [PMID: 34891767 DOI: 10.1109/embc46164.2021.9629788] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Parkinson's disease is the fastest growing neurological disorder worldwide. Traditionally, diagnosis and monitoring of its motor manifestations depend on examination of the speed, amplitude, and frequency of movement by trained providers. Despite the use of validated scales, clinical examination of movement is semi-quantitative, relatively subjective and it has become a major challenge during the ongoing pandemic. Using digital and technology-based tools during synchronous telehealth can overcome these barriers but it requires access to powerful computers and high-speed internet. In resource-limited settings without consistent access to trained providers, computers and internet, there is a need to develop accessible tools for telehealth application. We simulated a controlled asynchronous telehealth environment to develop and pre-test optical flow and inertial sensors (accelerometer and gyroscope) to assess sequences of 10 repetitive finger-tapping movements performed at a cued frequency of 1 Hz. In 42 sequences obtained from 7 healthy volunteers, we found positive correlations between the frequencies estimated by all modalities (ρ=0.63-0.93, P<0.01). Test-retest experiments showed median coefficients of variation of 7.04% for optical flow, 7.78% for accelerometer and 11.79% for gyroscope measures. This pilot study shows that combining optical flow and inertial sensors is a potential telehealth approach to accurately measure the frequency of repetitive finger movements.Clinical relevance- This pilot study presents a comparative analysis between inertial sensors and optical flow to characterize repetitive finger-tapping movements in healthy volunteers. These methods are feasible for the objective evaluation of bradykinesia as part of telehealth applications.
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Jan A, Gonçalves NP, Vaegter CB, Jensen PH, Ferreira N. The Prion-Like Spreading of Alpha-Synuclein in Parkinson's Disease: Update on Models and Hypotheses. Int J Mol Sci 2021; 22:8338. [PMID: 34361100 PMCID: PMC8347623 DOI: 10.3390/ijms22158338] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 07/29/2021] [Accepted: 07/30/2021] [Indexed: 12/11/2022] Open
Abstract
The pathological aggregation of the presynaptic protein α-synuclein (α-syn) and propagation through synaptically coupled neuroanatomical tracts is increasingly thought to underlie the pathophysiological progression of Parkinson's disease (PD) and related synucleinopathies. Although the precise molecular mechanisms responsible for the spreading of pathological α-syn accumulation in the CNS are not fully understood, growing evidence suggests that de novo α-syn misfolding and/or neuronal internalization of aggregated α-syn facilitates conformational templating of endogenous α-syn monomers in a mechanism reminiscent of prions. A refined understanding of the biochemical and cellular factors mediating the pathological neuron-to-neuron propagation of misfolded α-syn will potentially elucidate the etiology of PD and unravel novel targets for therapeutic intervention. Here, we discuss recent developments on the hypothesis regarding trans-synaptic propagation of α-syn pathology in the context of neuronal vulnerability and highlight the potential utility of novel experimental models of synucleinopathies.
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Affiliation(s)
- Asad Jan
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark; (N.P.G.); (C.B.V.); (P.H.J.)
| | - Nádia Pereira Gonçalves
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark; (N.P.G.); (C.B.V.); (P.H.J.)
- International Diabetic Neuropathy Consortium (IDNC), Aarhus University Hospital, 8200 Aarhus, Denmark
| | - Christian Bjerggaard Vaegter
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark; (N.P.G.); (C.B.V.); (P.H.J.)
- International Diabetic Neuropathy Consortium (IDNC), Aarhus University Hospital, 8200 Aarhus, Denmark
| | - Poul Henning Jensen
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark; (N.P.G.); (C.B.V.); (P.H.J.)
| | - Nelson Ferreira
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Department of Biomedicine, Aarhus University, 8000 Aarhus, Denmark; (N.P.G.); (C.B.V.); (P.H.J.)
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