Sansone Luigi Editor at IgMin Research
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Biography
Sansone Luigi graduated in 2009 in Molecular and Cellular Biology from the Tor Vergata University of Rome. From 2006 to 2009 he worked as an intern in the laboratory of Prof. Mauro Piacentini with research activities on endoplasmic reticulum stress. In June 2021 he won the competition for fixed-term researcher L.240/10 type A 05/F1 - APPLIED BIOLOGY BIO/13 - Applied biology San Raffaele Telematic University of Rome Department of Promotion of Human Sciences and Quality of Life.
Research Interest
Investigation of the mechanisms that regulate cell death and degradation with loss of function in neurodegenerative diseases. He has also carried out research on the function of mitochondrial sirtuins and on the recovery of mitochondrial integrity and cell survival in the presence of stress.
Editor
Work Details
Professor
San Raffaele University of Rome
Department of Human Sciences and Promotion of the Quality of Life
Italy
Contribution by Topic Area
Why Publish with us?
Global Visibility – Indexed in major databases
Fast Peer Review – Decision within 14–21 days
Open Access – Maximize readership and citation
Multidisciplinary Scope – Biology, Medicine and Engineering
Editorial Board Excellence – Global experts involved
University Library Indexing – Via OCLC
Permanent Archiving – CrossRef DOI
APC – Affordable APCs with discounts
Citation – High Citation Potential
Which articles are now trending?
Research Articles
- Enhancing Missing Values Imputation through Transformer-Based Predictive Modeling
- Integrated Multi-fidelity Structural Optimization for UAV Wings
- Maternal Knowledge and Practices in Caring for Children under Five with Pneumonia: A Cross-Sectional Study in Vietnam
- Sensor-based Sorting using De-XRT Sensor Applied to a Greenfield Copper Ore Project in Southern Brazil
- Efficacy of Different Concentrations of Insect Growth Regulators (IGRs) on Maize Stem Borer Infestation
- Analytical Expressions of the Markov Chain of K-Ras4B Protein within the Catalytic Environment and a New Markov-State Model
