Carlo Pozzilli

Full Professor

Sapienza University of Rome

Italy

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About

Carlo Pozzilli is a Full Professor at Sapienza University of Rome, Italy. His research primarily focuses on multiple sclerosis, with recent publications addressing various aspects such as regulatory implications of generics and biosimilars, advanced MRI techniques for diagnosis, and the safety and efficacy of treatments like alemtuzumab and ponesimod. Additionally, he has explored the impact of COVID-19 on multiple sclerosis patients, contributing to the understanding of this complex neurological condition.

Articles (14)

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2025

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2024

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2021

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2021

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2021

Collaborators (5)

Alasdair Coles

University of Cambridge

UNITED KINGDOM

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Alessandra Scaravilli

University of Naples Federico II

ITALY

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Sirio Cocozza

ITALY

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Helene Zephir

Professor of Neurology

FRANCE

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Patrizia Pantano

Sapienza University of Rome

ITALY

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Carlo Pozzilli

Research Interests

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Articles (14)

Generics, Biosimilars and Follow‐On Non‐Biologic Complex Drugs for Multiple Sclerosis: A Narrative Review of the Regulatory and Clinical Implications for European Neurologists

Radiological Reporting Systems in Multiple Sclerosis

Advanced MRI Techniques: Diagnosis and Follow-Up of Multiple Sclerosis

Diagnosis and treatment of progressive multiple sclerosis: A position paper

Multiple sclerosis patients treated with cladribine tablets: expert opinion on practical management after year 4

Safety and efficacy with alemtuzumab over 13 years in relapsing-remitting multiple sclerosis: final results from the open-label TOPAZ study

Relation of sensorimotor and cognitive cerebellum functional connectivity with brain structural damage in patients with multiple sclerosis and no disability

DMTs and Covid‐19 severity in MS: a pooled analysis from Italy and France

Ponesimod to treat multiple sclerosis.

COVID-19 Severity in Multiple Sclerosis: Putting Data Into Context.

Case Report: Multiple Sclerosis Relapses After Vaccination Against SARS-CoV2: A Series of Clinical Cases.

A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis.

A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis

Prognostic Accuracy of NEDA-3 in Long-term Outcomes of Multiple Sclerosis.

Collaborators (5)

Alasdair Coles

Alessandra Scaravilli

Sirio Cocozza

Helene Zephir

Patrizia Pantano

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