MOJGAN MASOODI

Associate Professor

Inselspital Universitätsspital Bern

Switzerland

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About

Dr. Mojgan Masoodi is an Associate Professor at Inselspital Universitätsspital Bern, Switzerland. Her research focuses on lipidomics, metabolic dysfunction-associated steatohepatitis, and the application of machine learning in precision medicine. Recent publications highlight her work on the metabolic phenotyping of liver disease and the exploration of hepatic lipid alterations through advanced imaging techniques.

Articles (13)

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2025

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2025

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2024

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2024

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2024

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2024

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2023

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2023

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2023

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2022

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2021

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2020

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2020

Collaborators (8)

Ren-Wang Peng

Professor

SWITZERLAND

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Matthew J. Sweet

University of Queensland

AUSTRALIA

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Aurel Perren

SWITZERLAND

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Elizabeth Powell

University of Queensland

AUSTRALIA

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Frédéric Gachon

Associate Professor

University of Queensland Institute for Molecular Bioscience

AUSTRALIA

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Quentin M. Anstee

Newcastle University

UNITED KINGDOM

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Jaime Bosch

Emeritus Professor of Medicine

University of Barcelona

SPAIN

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Joanne Verheij

Prof. dr.

Amsterdam UMC

NETHERLANDS

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MOJGAN MASOODI

Research Interests

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About

Articles (13)

Skylite: Skyline-Based Lipid Isomer Retention Time Evaluation for Lipidomics in Metabolic Dysfunction-Associated Steatohepatitis

Ubiquinol-mediated suppression of mitochondria-associated ferroptosis is a targetable function of lactate dehydrogenase B in cancer

Metabolic phenotyping of patients with advanced chronic liver disease for better characterization of cirrhosis regression

Machine learning applications in precision medicine: Overcoming challenges and unlocking potential

Spatial lipidomics reveals zone-specific hepatic lipid alteration and remodeling in metabolic dysfunction-associated steatohepatitis

Transcriptomics-driven metabolic pathway analysis reveals similar alterations in lipid metabolism in mouse MASH model and human

Characterisation of hepatic lipid signature distributed across the liver zonation using mass spectrometry imaging

Metabolic Pathway Analysis: Advantages and Pitfalls for the Functional Interpretation of Metabolomics and Lipidomics Data

Machine learning algorithm improves detection of NASH (NAS-based) and at-risk NASH, a development and validation study.

Disruption of the circadian clock component BMAL1 elicits an endocrine adaption impacting on insulin sensitivity and liver disease

Metabolomics and lipidomics in NAFLD: biomarkers and non-invasive diagnostic tests

Exploring Valine Metabolism in Astrocytic and Liver Cells: Lesson from Clinical Observation in TBI Patients for Nutritional Intervention

Untargeted Profiling of Bile Acids and Lysophospholipids Identifies the Lipid Signature Associated with Glycemic Outcome in an Obese Non-Diabetic Clinical Cohort

Collaborators (8)

Ren-Wang Peng

Matthew J. Sweet

Aurel Perren

Elizabeth Powell

Frédéric Gachon

Quentin M. Anstee

Jaime Bosch

Joanne Verheij

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