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Marco Salvetti

Researcher at Sapienza University of Rome

Publications -  294
Citations -  13327

Marco Salvetti is an academic researcher from Sapienza University of Rome. The author has contributed to research in topics: Multiple sclerosis & Medicine. The author has an hindex of 44, co-authored 263 publications receiving 10931 citations. Previous affiliations of Marco Salvetti include Max Planck Society & Istituto Neurologico Mediterraneo.

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Brainstem signs with progressing atrophy of medulla oblongata and upper cervical spinal cord.

TL;DR: A 20-year-old man who was admitted to hospital in May, 1998, because of episodic dysphagia and dysphonia became permanent and the patient developed diplopia due to right VIth nerve palsy in 1999, and MRI showed slowly progressing medullary atrophy and persistent contrast enhancement.
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Italian multicentre observational study of the prevalence of CCSVI in multiple sclerosis (CoSMo study): rationale, design, and methodology.

TL;DR: The rationale, design, and methodology adopted in the CoSMo study, conducted with the aim of verifying whether or not CCSVI is linked to MS, are presented and the link between CCSvi and the severity of MS is examined.
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Structural Brain Correlates of Neurourologic Abnormalities in Multiple Sclerosis

TL;DR: Subjects with urinary symptoms exhibited greater overall functional disability and a higher midbrain MRI-weighted lesion score than asymptomatic patients, and no statistically significant group differences were found for the other brain regions.
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A genetic variant of the anti-apoptotic protein Akt predicts natalizumab-induced lymphocytosis and post-natalizumab multiple sclerosis reactivation.

TL;DR: This study identified one functionally meaningful genetic variant within the Akt signaling pathway that is associated with both lymphocyte count and composition alterations during natalizumab treatment, and with the risk of disease reactivation after natalIZumab discontinuation.
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Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study

TL;DR: This work presents proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone.