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[FG] D'Souza Marcus

Publications

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Oechtering, Johanna et al. (2025) ‘Aberrant Complement Activation Is Associated With Structural Brain Damage in Multiple Sclerosis’, Neurology Neuroimmunology & Neuroinflammation, 12(2). Available at: https://doi.org/10.1212/nxi.0000000000200361.

Disanto, Giulio et al. (2025) ‘Treatment persistence and clinical outcomes in patients starting B cell depleting therapies within the Swiss MS Cohort’, Multiple Sclerosis Journal - Experimental, Translational and Clinical, 11(1). Available at: https://doi.org/10.1177/20552173251315457.

Papadopoulou, Athina et al. (2024) ‘Visual evoked potentials in multiple sclerosis: P100 latency and visual pathway damage including the lateral geniculate nucleus’, Clinical Neurophysiology. 20.02.2024, 161, pp. 122–132. Available at: https://doi.org/10.1016/j.clinph.2024.02.020.

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Mallucci, Giulia et al. (2024) ‘Neurostatus-SMARTCARE clinical trial: Enabling health care professionals to assess EDSS for decentralized trials in multiple sclerosis’, Multiple Sclerosis Journal [Preprint]. Available at: https://doi.org/10.1177/13524585241305966.

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Reissenberger, Pamela et al. (2023) ‘Determine atrial fibrillation burden with a photoplethysmographic mobile sensor: the atrial fibrillation burden trial: detection and quantification of episodes of atrial fibrillation using a cloud analytics service connected to a wearable with photoplethysmographic sensor’, European Heart Journal - Digital Health, 4(5), pp. 402–410. Available at: https://doi.org/10.1093/ehjdh/ztad039.

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Cagol A et al. (2023) ‘Optical coherence tomography reflects clinically relevant gray matter damage in patients with multiple sclerosis’, Journal of Neurology, 270(4), pp. 2139–2148. Available at: https://doi.org/10.1007/s00415-022-11535-8.

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Lam KH et al. (2022) ‘Improving Detection of Change in Motor Functioning in Multiple Sclerosis Using Video-Assisted Composite Measures.’, International journal of MS care, 24(5), pp. 230–234. Available at: https://doi.org/10.7224/1537-2073.2021-044.

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Bergamaschi R et al. (2022) ‘Description and preliminary experience with Virtual Visit Assessment (ViVA) during the COVID-19 pandemic, a structured virtual management protocol for patients with multiple sclerosis’, Neurological Sciences, 43(2), pp. 1207–1214. Available at: https://doi.org/10.1007/s10072-021-05371-3.

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Correale L et al. (2021) ‘Effects of Combined Endurance and Resistance Training in Women With Multiple Sclerosis: A Randomized Controlled Study’, Frontiers in Neurology, 12, p. 698460. Available at: https://doi.org/10.3389/fneur.2021.698460.

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Bucello S et al. (2021) ‘Real world experience with teriflunomide in multiple sclerosis: the TER-Italy study’, Journal of Neurology, 268(8), pp. 2922–2932. Available at: https://doi.org/10.1007/s00415-021-10455-3.

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Businaro P et al. (2021) ‘COVID-19 in patients with myasthenia gravis: Epidemiology and disease course’, Muscle and Nerve, 64(2), pp. 206–211. Available at: https://doi.org/10.1002/mus.27324.

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Mallucci G et al. (2021) ‘Circulating endothelial progenitor cells during pregnancy in multiple sclerosis’, Neurological Sciences, 42(4), pp. 1443–1451. Available at: https://doi.org/10.1007/s10072-020-04648-3.

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Mallucci G et al. (2021) ‘Safety of disease-modifying treatments in SARS-CoV-2 antibody-positive multiple sclerosis patients’, Multiple Sclerosis and Related Disorders, 49, p. 102754. Available at: https://doi.org/10.1016/j.msard.2021.102754.

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D’Souza M et al. (2021) ‘Standardization and digitization of clinical data in multiple sclerosis’, Nature Reviews Neurology, 17(2), pp. 119–125. Available at: https://doi.org/10.1038/s41582-020-00448-7.

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Bergamaschi R et al. (2021) ‘PM2.5 exposure as a risk factor for multiple sclerosis. An ecological study with a Bayesian mapping approach’, Environmental Science and Pollution Research, 28(3), pp. 2804–2809. Available at: https://doi.org/10.1007/s11356-020-10595-5.

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Papadopoulou A et al. (2021) ‘Lateral geniculate nucleus volume changes after optic neuritis in neuromyelitis optica: A longitudinal study’, NeuroImage: Clinical, 30, p. 102608. Available at: https://doi.org/10.1016/j.nicl.2021.102608.

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Cortese A et al. (2020) ‘Air pollution as a contributor to the inflammatory activity of multiple sclerosis’, Journal of Neuroinflammation, 17(1), p. 334. Available at: https://doi.org/10.1186/s12974-020-01977-0.

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Gastaldi M et al. (2020) ‘Cell-based assays for the detection of MOG antibodies: a comparative study’, Journal of Neurology, 267(12), pp. 3555–3564. Available at: https://doi.org/10.1007/s00415-020-10024-0.

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Mallucci G et al. (2020) ‘Asymptomatic SARS-CoV-2 infection in two patients with multiple sclerosis treated with fingolimod’, 45. Available at: https://doi.org/10.1016/j.msard.2020.102414.

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Steinheimer S et al. (2020) ‘Setwise comparison: efficient fine-grained rating of movement videos using algorithmic support–a proof of concept study’, Disability and Rehabilitation, 42(18), pp. 2640–2646. Available at: https://doi.org/10.1080/09638288.2018.1563832.

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D’Souza M et al. (2020) ‘Electronic Neurostatus-EDSS increases the quality of expanded disability status scale assessments: Experience from two phase 3 clinical trials’, Multiple Sclerosis Journal, 26(8), pp. 993–996. Available at: https://doi.org/10.1177/1352458519845108.

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Kaufmann M et al. (2020) ‘Development and validation of the self-reported disability status scale (SRDSS) to estimate EDSS-categories’, Multiple Sclerosis and Related Disorders, 42, p. 102148. Available at: https://doi.org/10.1016/j.msard.2020.102148.

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D’Souza M et al. (2020) ‘Diagnostic procedures in suspected attacks in patients with neuromyelitis optica spectrum disorders: Results of an international survey’, Multiple Sclerosis and Related Disorders, 41, p. 102027. Available at: https://doi.org/10.1016/j.msard.2020.102027.

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Pistono C et al. (2020) ‘Unraveling a new player in multiple sclerosis pathogenesis: The RNA-binding protein HuR’, Multiple Sclerosis and Related Disorders, 41, p. 102048. Available at: https://doi.org/10.1016/j.msard.2020.102048.

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Bergamaschi R et al. (2020) ‘Increased prevalence of multiple sclerosis and clusters of different disease risk in Northern Italy’, Neurological Sciences, 41(5), pp. 1089–1095. Available at: https://doi.org/10.1007/s10072-019-04205-7.

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D’Souza M et al. (2020) ‘Autoencoder as a new method for maintaining data privacy while analyzing videos of patients with motor dysfunction: Proof-of-concept study’, Journal of Medical Internet Research, 22(5), p. e16669. Available at: https://doi.org/10.2196/16669.

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Mallucci G et al. (2020) ‘A method to compare prospective and historical cohorts to evaluate drug effects. Application to the analysis of early treatment effectiveness of intramuscular interferon-β1a in multiple sclerosis patients’, Multiple Sclerosis and Related Disorders, 40, p. 101952. Available at: https://doi.org/10.1016/j.msard.2020.101952.

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Burggraaff J et al. (2020) ‘Video-Based Pairwise Comparison: Enabling the Development of Automated Rating of Motor Dysfunction in Multiple Sclerosis’, Archives of Physical Medicine and Rehabilitation, 101(2), pp. 234–241. Available at: https://doi.org/10.1016/j.apmr.2019.07.016.

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Cortese A et al. (2020) ‘Cerebellar ataxia, neuropathy, vestibular areflexia syndrome due to RFC1 repeat expansion’, Brain, 143(2), pp. 489–490. Available at: https://doi.org/10.1093/brain/awz418.

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Pistono C et al. (2020) ‘Response to oxidative stress of peripheral blood mononuclear cells from multiple sclerosis patients and healthy controls’, Cell Stress and Chaperones, 25(1), pp. 81–91. Available at: https://doi.org/10.1007/s12192-019-01049-0.

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Paolicelli D et al. (2019) ‘Retrospectively acquired cohort study to evaluate the long-term impact of two different treatment strategies on disability outcomes in patients with relapsing multiple sclerosis (RE.LO.DI.MS): data from the Italian MS Register’, Journal of Neurology, 266(12), pp. 3098–3107. Available at: https://doi.org/10.1007/s00415-019-09531-6.

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Baroncini D et al. (2019) ‘Impact of natural menopause on multiple sclerosis: A multicentre study’, Journal of Neurology, Neurosurgery and Psychiatry, 90(11), pp. 1201–1206. Available at: https://doi.org/10.1136/jnnp-2019-320587.

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D’Souza, Marcus et al. (2019) ‘Autoencoder as a New Method for Maintaining Data Privacy While Analyzing Videos of Patients With Motor Dysfunction: Proof-of-Concept Study (Preprint)’, J Med Internet Res. [Preprint]. Available at: https://doi.org/10.2196/preprints.16669.

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van Munster CE et al. (2019) ‘Tasks of activities of daily living (ADL) are more valuable than the classical neurological examination to assess upper extremity function and mobility in multiple sclerosis’, Multiple Sclerosis Journal. 31.08.2018, 25(12), pp. 1673–1681. Available at: https://doi.org/10.1177/1352458518796690.

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Baroncini D et al. (2019) ‘Long-term follow-up of pediatric MS patients starting treatment with injectable first-line agents: A multicentre, Italian, retrospective, observational study’, Multiple Sclerosis Journal, 25(3), pp. 399–407. Available at: https://doi.org/10.1177/1352458518754364.

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Maurelli M et al. (2018) ‘Interferon-beta injection site reactions in patients with multiple sclerosis’, Journal of Dermatological Treatment, 29(8), pp. 831–834. Available at: https://doi.org/10.1080/09546634.2018.1467539.

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Bergamaschi R et al. (2018) ‘Air pollution is associated to the multiple sclerosis inflammatory activity as measured by brain MRI’, Multiple Sclerosis Journal, 24(12), pp. 1578–1584. Available at: https://doi.org/10.1177/1352458517726866.

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Mallucci G et al. (2018) ‘Two-year real-life efficacy, tolerability and safety of dimethyl fumarate in an Italian multicentre study’, Journal of Neurology, 265(8), pp. 1850–1859. Available at: https://doi.org/10.1007/s00415-018-8916-6.

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D’Souza M et al. (2018) ‘Reference videos reduce variability of motor dysfunction assessments in multiple sclerosis’, Multiple Sclerosis Journal - Experimental, Translational and Clinical. 09.08.2018, 4(3), p. 2055217318792399. Available at: https://doi.org/10.1177/2055217318792399.

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Parmar K et al. (2018) ‘The role of the cerebellum in multiple sclerosis—150 years after Charcot’, Neuroscience and Biobehavioral Reviews, 89, pp. 85–98. Available at: https://doi.org/10.1016/j.neubiorev.2018.02.012.

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Peruzzotti-Jametti L et al. (2018) ‘Macrophage-Derived Extracellular Succinate Licenses Neural Stem Cells to Suppress Chronic Neuroinflammation’, Cell Stem Cell, 22(3), pp. 355–368.e13. Available at: https://doi.org/10.1016/j.stem.2018.01.020.

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Alessandrino F et al. (2018) ‘Do MRI structured reports for multiple sclerosis contain adequate information for clinical decision making?’, American Journal of Roentgenology, 210(1), pp. 24–29. Available at: https://doi.org/10.2214/AJR.17.18451.

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Pistono C et al. (2017) ‘What’s new about oral treatments in Multiple Sclerosis? Immunogenetics still under question’, Pharmacological Research, 120, pp. 279–293. Available at: https://doi.org/10.1016/j.phrs.2017.03.025.

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D’Souza M et al. (2017) ‘Neurostatus e-Scoring improves consistency of Expanded Disability Status Scale assessments: A proof of concept study’, Multiple Sclerosis, 23(4), pp. 597–603. Available at: https://doi.org/10.1177/1352458516657439.

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Hauser SL et al. (2017) ‘Ocrelizumab versus interferon beta-1a in relapsing multiple sclerosis’, New England Journal of Medicine. 21.12.2016, 376(3), pp. 221–234. Available at: https://doi.org/10.1056/NEJMoa1601277.

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Montalban,Xavier et al. (2017) ‘Ocrelizumab versus placebo in primary progressive multiple sclerosis’, New England Journal of Medicine. 21.12.2016, 376(3), pp. 209–220. Available at: https://doi.org/10.1056/NEJMoa1606468.

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Boiocchi C et al. (2016) ‘Heat shock protein 70-hom gene polymorphism and protein expression in multiple sclerosis’, Journal of Neuroimmunology, 298, pp. 189–193. Available at: https://doi.org/10.1016/j.jneuroim.2016.07.011.

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Sarkar A. et al. (2016) ‘Setwise comparison: Consistent, scalable, continuum labels for computer vision’. Association for Computing Machineryacmhelp@acm.org, pp. 261–271. Available at: https://doi.org/10.1145/2858036.2858199.

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Schlaeger, Regina et al. (2016) ‘Monitoring multiple sclerosis by multimodal evoked potentials : numerically versus ordinally scaled scoring systems’, Clinical neurophysiology, 127(3), pp. 1864–71. Available at: https://doi.org/10.1016/j.clinph.2015.11.041.

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Tambalo S et al. (2015) ‘Functional magnetic resonance imaging of rats with experimental autoimmune encephalomyelitis reveals brain cortex remodeling’, Journal of Neuroscience, 35(27), pp. 10088–10100. Available at: https://doi.org/10.1523/JNEUROSCI.0540-15.2015.

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Morrison C et al. (2015) ‘Usability and Acceptability of ASSESS MS: Assessment of Motor Dysfunction in Multiple Sclerosis Using Depth-Sensing Computer Vision.’, JMIR human factors. 24.06.2015, 2(1), p. e11. Available at: https://doi.org/10.2196/humanfactors.4129.

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Bergamaschi R et al. (2015) ‘BREMSO: A simple score to predict early the natural course of multiple sclerosis’, European Journal of Neurology, 22(6), pp. 981–989. Available at: https://doi.org/10.1111/ene.12696.

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Kappos L et al. (2015) ‘On the origin of Neurostatus’, 4(3). Available at: https://doi.org/10.1016/j.msard.2015.04.001.

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Mallucci G et al. (2015) ‘The role of immune cells, glia and neurons in white and gray matter pathology in multiple sclerosis’, Progress in Neurobiology, 127-128, pp. 1–22. Available at: https://doi.org/10.1016/j.pneurobio.2015.02.003.

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D’Souza M (2015) ‘MS tracking: basic and new tools/exergaming’, Neurodegenerative disease management, 5(6), pp. 47–49. Available at: https://doi.org/10.2217/nmt.15.60.

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Schaeffer J. et al. (2015) ‘Multiple Sclerosis’. Elsevier Inc., pp. 497–520. Available at: https://doi.org/10.1016/b978-0-12-398270-4.00030-6.

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Peruzzotti-Jametti L et al. (2014) ‘The role of the immune system in central nervous system plasticity after acute injury’, Neuroscience, 283, pp. 210–221. Available at: https://doi.org/10.1016/j.neuroscience.2014.04.036.

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Kontschieder P et al. (2014) ‘Quantifying progression of multiple sclerosis via classification of depth videos’, Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17(Pt 2), pp. 429–437. Available at: https://doi.org/10.1007/978-3-319-10470-6_54.

Papadopoulou, A. et al. (2014) ‘Lesion-to-ventricle distance and other risk factors for the persistence of newly formed black holes in relapsing-remitting multiple sclerosis’, Multiple Sclerosis Journal, 20(3), pp. 322–30. Available at: https://doi.org/10.1177/1352458513495583.

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Schlaeger, R et al. (2014) ‘Prediction of MS disability by multimodal evoked potentials : investigation during relapse or in the relapse-free interval?’, Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 125(9), pp. 1889–92. Available at: https://doi.org/10.1016/j.clinph.2013.12.117.

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Schlaeger, Regina et al. (2014) ‘Electrophysiological markers and predictors of the disease course in primary progressive multiple sclerosis’, Multiple Sclerosis Journal, 20(1), pp. 51–6. Available at: https://doi.org/10.1177/1352458513490543.

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D`Souza,Marcus, Papadopoulou,A and Kappos,Ludwig (2014) ‘Bedeutung des EDSS für die Therapie-indikation bei Multipler Sklerose’, 12(4), pp. 6–9.

Mallucci G et al. (2012) ‘Anti-aquaporin-4 antibody-positive recurrent isolated optic neuritis and primary Sjögren’s syndrome’, Journal of Neurology, 259(8). Available at: https://doi.org/10.1007/s00415-012-6434-5.

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Schlaeger, R. et al. (2012) ‘Prediction of long-term disability in multiple sclerosis’, Multiple Sclerosis Journal, 18(1), pp. 31–8. Available at: https://doi.org/10.1177/1352458511416836.

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Schlaeger, Regina et al. (2012) ‘Combined evoked potentials as markers and predictors of disability in early multiple sclerosis’, Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 123(2), pp. 406–10. Available at: https://doi.org/10.1016/j.clinph.2011.06.021.

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Grossman, P et al. (2010) ‘MS quality of life, depression, and fatigue improve after mindfulness training: a randomized trial’, Neurology : journal of the American Academy of Neurology, 75(13), pp. 1141–9. Available at: https://doi.org/10.1212/wnl.0b013e3181f4d80d.

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Papadopoulou, Athina et al. (2010) ‘Dimethyl fumarate for multiple sclerosis’, Expert opinion on investigational drugs, 19(12), pp. 1603–12. Available at: https://doi.org/10.1517/13543784.2010.534778.

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D’Souza, Marcus et al. (2008) ‘Intravenous thrombolysis cancelled in acute right hemiparesis’, Cerebrovascular Diseases, 26(6), pp. 665–6. Available at: https://doi.org/10.1159/000172974.

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D’Souza, M, Kappos, L and Czaplinski, A (2008) ‘Reconsidering clinical outcomes in Multiple Sclerosis : relapses, impairment, disability and beyond’, Journal of the neurological sciences, 274(1-2), pp. 76–9. Available at: https://doi.org/10.1016/j.jns.2008.08.023.

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Borgwardt SJ et al. (2007) ‘Structural brain abnormalities in individuals with an at-risk mental state who later develop psychosis’, British Journal of Psychiatry, 191(SUPPL. 51), pp. s69–75. Available at: https://doi.org/10.1192/bjp.191.51.s69.

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Borgwardt SJ et al. (2007) ‘Regional Gray Matter Volume Abnormalities in the At Risk Mental State’, Biological Psychiatry. 13.11.2006, 61(10), pp. 1148–1156. Available at: https://doi.org/10.1016/j.biopsych.2006.08.009.

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Epplen C et al. (1997) ‘Genetic predisposition to multiple sclerosis as revealed by immunoprinting’, Annals of Neurology, 41(3), pp. 341–352. Available at: https://doi.org/10.1002/ana.410410309.

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Eng CM et al. (1997) ‘Fabry disease: Thirty-five mutations in the α-galactosidase a gene in patients with classic and variant phenotypes’, Molecular Medicine, 3(3), pp. 174–182. Available at: https://doi.org/10.1007/bf03401671.

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Epplen C et al. (1995) ‘Immunoprinting reveals different genetic bases for (auto)immune diseases’, ELECTROPHORESIS, 16(1), pp. 1693–1697. Available at: https://doi.org/10.1002/elps.11501601281.

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