Modeling multiple sclerosis using mobile and wearable sensor data

Gashi, Shkurta; Oldrati, Pietro; Moebus, Max; Hilty, Marc; Barrios, Liliana; Ozdemir, Firat; ,; Kana, Veronika; Lutterotti, Andreas; Rätsch, Gunnar; Holz, Christian

doi:10.1038/s41746-024-01025-8

Cited by 2 publications

(1 citation statement)

References 56 publications

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“…23 Measurements Based on the Smartphone App and Wearable Sensors Patients were asked to install a custom-built application on their smartphones. 24 Furthermore, each patient received a wearable armband device (Everion, manufactured by Biovotion AG). Patients were instructed on how to use the device during a hospital visit 3 months before visit 1.…”

Section: Questionnairesmentioning

confidence: 99%

Clinical and Immunologic Effects of Paraprobiotics in Long-COVID Patients

Docampo,

Batruch,

Oldrati

et al. 2024

Neurol Neuroimmunol Neuroinflamm

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Background and ObjectivesAfter the enormous health burden during the acute stages of the COVID-19 pandemic, we are now facing another important challenge, that is, long-COVID, a clinical condition with often disabling signs and symptoms of the neuropsychiatric, gastrointestinal, respiratory, cardiovascular, and immune systems. While the pathogenesis of this syndrome is still poorly understood, alterations of immune function and the gut microbiota seem to play important roles. Because affected individuals are frequently unable to work for prolonged periods and suffer numerous health compromises, effective treatments represent a major unmet medical need. Multiple potential therapies have been tried, but none is approved yet. Approaches that are able to influence the immune system and gut microbiota such as probiotics and paraprobiotics, i.e., nonviable probiotics, seem promising candidates. We, therefore, evaluated the clinical and immunologic effects of paraprobiotics in a small pilot study. MethodsA total of 6 patients with long-COVID were followed systematically for more than 12 months after disease onset using standardized validated questionnaires, a smartphone app, and wearable sensors to assess neurocognitive function, fatigue, depressiveness, autonomic nervous system alterations, and quality of life. We then offered patients defined paraprobiotics for 4 weeks and evaluated them at the end of the treatment period using the same questionnaires, smartphone app, and wearable sensors. In addition, a comprehensive immunophenotyping and gut microbiota analysis was performed before and after treatment. ResultsImprovements in several of the neurologic symptoms such as dysautonomia, fatigue, and depression were documented using both patient-reported outcomes and data from the smartphone app and wearable sensors. Of interest, the expression of activation markers on some immune cell populations such as B cells and nonclassical monocytes and the expression of toll-like receptor 2 (TLR2) on T cells were reduced after paraprobiotics treatment. DiscussionOur results suggest that paraprobiotics might exert positive effects in patients with long-COVID most likely by modulating immune cell activation and expression of TLR2 on T cells. Further studies with paraprobiotics should confirm the promising observations of this small pilot study and hopefully not only improve the outcome of long-COVID but also unravel the pathomechanisms of this condition. MORE ONLINE Class of EvidenceCriteria for rating therapeutic and diagnostic studies NPub.org/coeFrom the Neuroimmunology and MS Research (nims) (M.

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Section: Questionnairesmentioning

confidence: 99%

Clinical and Immunologic Effects of Paraprobiotics in Long-COVID Patients

Docampo,

Batruch,

Oldrati

et al. 2024

Neurol Neuroimmunol Neuroinflamm

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Artificial Intelligence and Multiple Sclerosis

Amin,

Martínez-Heras,

Ontaneda

et al. 2024

Curr Neurol Neurosci Rep

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In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.

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Modeling multiple sclerosis using mobile and wearable sensor data

Cited by 2 publications

References 56 publications

Clinical and Immunologic Effects of Paraprobiotics in Long-COVID Patients

Clinical and Immunologic Effects of Paraprobiotics in Long-COVID Patients

Artificial Intelligence and Multiple Sclerosis

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