Giulia Russo

My mission is to apply computational approaches in a multidisciplinary environment in order to speed-up the drug discovery process, optimise in vitro and in vivo assays, ehance the therapeutic response in cancer and immunotherapies field, and to solve, in general, biomedical issues.

EDUCATION

PhD in Basic and Applied Biomedical Sciences

RESEARCH INTERESTS

Computational modeling in biomedicine and systems biology

Scopus Publications

Comment on “A decade of thermostatted kinetic theory models for complex active matter living systems” by Carlo Bianca
Francesco Pappalardo and Giulia Russo
Elsevier BV



In vitro approaches to investigate the effect of chemicals on antibody production: the case study of PFASs
Martina Iulini, Valeria Bettinsoli, Ambra Maddalon, Valentina Galbiati, Aafke W. F. Janssen, Karsten Beekmann, Giulia Russo, Francesco Pappalardo, Styliani Fragki, Alicia Paini,et al.
Springer Science and Business Media LLC
Abstract The increasing variety and quantity of new chemical substances have raised concerns about their potential immunotoxic effects, making it essential to assess their impact on human health. One key concern is the reduction of antibody production, as seen with per- and poly-fluoroalkyl substances (PFASs), commonly known as “forever chemicals.” Both in vivo and epidemiological data show that PFASs have immunosuppressive effects, leading to reduced antibody responses, particularly following vaccination. In animal studies, the T cell-dependent (TD) antibody response is the gold standard for assessing chemical effects on immune function. This study utilized two in vitro approaches to investigate the effects of chemicals on antibody production using human peripheral blood mononuclear cells. Initial tests used unstimulated, negative (vehicle), and positive (rapamycin) controls to confirm the robustness of the models. Subsequently, four long-chain PFASs (PFOA, PFOS, PFNA, and PFHxS) were tested. Keyhole limpet hemocyanin (KLH) was used to mimic the TD response, while a TLR9 agonist and IL-2 activated B cells for T cell-independent (TI) immunoglobulin production. The results demonstrated the ability to reproduce TD and TI responses in vitro with robust, reproducible outcomes across a cohort of 20 human donors. The data, consistent with existing literature, showed a significant reduction in anti-KLH IgM production, especially for PFOA in male donors. Similar trends were observed for all PFASs in suppressing total TI IgG and IgM production. These methods closely replicated in vivo conditions, offering a potential alternative to animal models in immunotoxicity assessments.


ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation—Methods and Results
Alessia Rondinella, Francesco Guarnera, Elena Crispino, Giulia Russo, Clara Di Lorenzo, Davide Maimone, Francesco Pappalardo, and Sebastiano Battiato
Springer Nature Switzerland



Advancing PFAS risk assessment: Integrative approaches using agent-based modelling and physiologically-based kinetic for environmental and health safety
Martina Iulini, Giulia Russo, Elena Crispino, Alicia Paini, Styliani Fragki, Emanuela Corsini, and Francesco Pappalardo
Elsevier BV



Pioneering bioinformatics with agent-based modelling: an innovative protocol to accurately forecast skin or respiratory allergic reactions to chemical sensitizers
Giulia Russo, Elena Crispino, Silvia Casati, Emanuela Corsini, Andrew Worth, and Francesco Pappalardo
Oxford University Press (OUP)
Abstract The assessment of the allergenic potential of chemicals, crucial for ensuring public health safety, faces challenges in accuracy and raises ethical concerns due to reliance on animal testing. This paper presents a novel bioinformatic protocol designed to address the critical challenge of predicting immune responses to chemical sensitizers without the use of animal testing. The core innovation lies in the integration of advanced bioinformatics tools, including the Universal Immune System Simulator (UISS), which models detailed immune system dynamics. By leveraging data from structural predictions and docking simulations, our approach provides a more accurate and ethical method for chemical safety evaluations, especially in distinguishing between skin and respiratory sensitizers. Our approach integrates a comprehensive eight-step process, beginning with the meticulous collection of chemical and protein data from databases like PubChem and the Protein Data Bank. Following data acquisition, structural predictions are performed using cutting-edge tools such as AlphaFold to model proteins whose structures have not been previously elucidated. This structural information is then utilized in subsequent docking simulations, leveraging both ligand–protein and protein–protein interactions to predict how chemical compounds may trigger immune responses. The core novelty of our method lies in the application of UISS—an advanced agent-based modelling system that simulates detailed immune system dynamics. By inputting the results from earlier stages, including docking scores and potential epitope identifications, UISS meticulously forecasts the type and severity of immune responses, distinguishing between Th1-mediated skin and Th2-mediated respiratory allergic reactions. This ability to predict distinct immune pathways is a crucial advance over current methods, which often cannot differentiate between the sensitization mechanisms. To validate the accuracy and robustness of our approach, we applied the protocol to well-known sensitizers: 2,4-dinitrochlorobenzene for skin allergies and trimellitic anhydride for respiratory allergies. The results clearly demonstrate the protocol’s ability to differentiate between these distinct immune responses, underscoring its potential for replacing traditional animal-based testing methods. The results not only support the potential of our method to replace animal testing in chemical safety assessments but also highlight its role in enhancing the understanding of chemical-induced immune reactions. Through this innovative integration of computational biology and immunological modelling, our protocol offers a transformative approach to toxicological evaluations, increasing the reliability of safety assessments.


A Head-to-Head Evaluation of a Novel Universal Influenza Vaccine Against Current Formulation: Implications for Future Immunization Strategies
Valentina Di Salvatore, Elena Crispino, Avisa Maleki, Giulia Russo, and Francesco Pappalardo
IEEE
Influenza remains a significant public health concern, with annual vaccine formulations traditionally developed based on the most prevalent virus strains from the previous year. This process often results in mismatches between the vaccine and circulating strains, limiting efficacy. In this study, we utilize the UISS-FLU simulator to compare the effects of a novel influenza vaccine formulation, developed in our previous research, against this year's standard vaccine. We aim to evaluate the potential advantages of our formulation in terms of immunogenic response and protective efficacy. By leveraging in silico methodologies, we can enhance vaccine design and optimization, allowing for a more adaptive and responsive approach to influenza immunization. Our findings will provide insights into the future of vaccine development, showcasing how computational tools can improve public health outcomes by potentially yielding a universal vaccine solution.


Application of PBK models for long-chain PFAS to short-chain PFAS: A proposal for toxicokinetic evaluation and in vitro to in vivo extrapolation
Martina Iulini, Giulia Russo, Elena Crispino, Emanuela Corsini, Francesco Pappalardo, and Alicia Paini
IEEE
This study explored the possibility to adapt the physiologically based kinetic (PBK) model, originally developed for long-chain and long half-life per- and polyfluoroalkyl substances (PFASs) by the European Food Safety Authority (EFSA), to assess three short-chain and short half-life PFASs namely perfluorobutanoic acid (PFBA), perfluorohexanoic acid (PFHxA) and perfluorobutanesulfonic acid (PFBS). The aim was to estimate the plasma concentration of short-chain PFASs following repeated oral exposure and use this data to inform the Universal Immune System Simulator (UISS) model to predict effect on antibody response. In parallel, in vitro to in vivo extrapolation (QIVIVE) was conducted using values obtained from in vitro experiments. Results show the feasibility of applying established long-chain PFASs kinetic models to short-chain PFASs and to investigate their potential health impacts.


Predicting Skin Sensitizer Potency and Immune Response Using UISS-TOX: A Novel Approach for Assessing Allergenic Potential
Elena Crispino, Giulia Russo, Elisabetta Arcidiacono, Silvia Casati, Emanuela Corsini, Andrew Worth, and Francesco Pappalardo
IEEE
The aim of this study was to explore the sensitizing potency of several known skin allergens, focusing on their capacity to induce allergic contact dermatitis (ACD). Our approach involves different bioinformatic tools, including UISS-TOX, a simulation platform designed to predict the immune response following allergen exposure. Using eight well-characterized skin sensitizers, including pyridine and hexyl salicylate, we evaluated docking interactions with keratin and Toll-like receptors (TLRs), and we predicted B-cell epitopes, providing insights into potential antigenic sites. The results were integrated into UISS-TOX simulations to observe T helper cell and cytokine dynamics over time. The simulations revealed distinct Th1-mediated responses consistent with ACD, enabling not only the prediction of skin sensitizer potency but also the differentiation of response intensities among the sensitizers. Pyridine, for instance, demonstrated a higher Th1 activation and associated cytokine release than hexyl salicylate, aligning with its stronger sensitizing profile in literature. This study underscores UISS-TOX’s potential as a reliable in silico method for allergenicity prediction, aligning with New Approach Methodologies and reducing the need for animal testing.


Agent-Based Modeling of Cutaneous Lupus Erythematosus: Exploring Keratinocyte-Driven Mechanisms
Abdul Wahab, Giulia Russo, and Francesco Pappalardo
IEEE
Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease characterized by the immune system attacking the body’s own tissue, losing its ability to distinguish between foreign pathogens and healthy cells. This condition affects approximately 5 million individuals worldwide, leading to widespread tissue and organ damage, including the skin, joints, kidneys, cardiovascular system, and central nervous system. The pathology of SLE is influenced by a variety of predisposing factors such as genetic and epigenetic changes, environmental triggers, infections, and hormonal imbalances. One subtype of SLE is Cutaneous Lupus Erythematosus (CLE), where skin involvement serves as a significant manifestation of the disease. CLE is characterized by distinct serological and histological features, with ultraviolet (UV) radiation being a primary environmental trigger exacerbating disease severity. Epidemiological data indicate an annual incidence of approximately 4 cases per 100,100 persons for CLE, while 70–80% of SLE patients report skin involvement. In this work, we extend the capabilities of the Universal Immune System Simulator (UISS), an agent-based modeling framework, by incorporating a disease layer to simulate SLE progression with a focus on CLE. This enhancement emphasizes the role of keratinocytes in disease development, offering deeper insights into the mechanisms underlying CLE pathology.


Unintended Risks of mRNA COVID-19 Vaccines: A UISS Simulation Study on Immune and Organ Health
Valentina Di Salvatore, Giulia Russo, and Francesco Pappalardo
IEEE
In 2019, humanity witnessed one of the most violent and dangerous pandemics ever seen: SARS-CoV-2 emerged suddenly, spreading rapidly across the world. The speed of its spread and the severity of its infections—with clinical symptoms ranging from flu-like manifestations to various forms of pneumonia, and even death due to acute respiratory distress syndrome (ARDS)—made SARS-CoV-2 one of the deadliest members of the coronavirus family. This triggered an urgent, global search for new technologies capable of curbing its spread and mitigating its effects as quickly as possible. In this context, mRNA vaccines established themselves as the most promising solution for a rapid response, even though the long-term effects of using this technology in vaccines remained unclear. Here, we shed light on the mechanism of action and potential side effects of mRNA vaccines, using the Universal Immune System Simulator (UISS) for in silico simulation of the human immune response.


Breaking the Cycle: Advancements in Universal Influenza Vaccine Design
Valentina Di Salvatore, Elena Crispino, Giulia Russo, and Francesco Pappalardo
IEEE
The term Influenza refers to a group of infectious respiratory diseases caused by influenza viruses: nowadays it presents a significant global health challenge due to its dynamic nature and propensity for rapid evolution. Multiple subtypes of influenza A viruses circulate in various hosts, including humans, birds, and mammals. The efficacy of influenza vaccines is influenced by the antigenic match between the vaccine strains and circulating viruses. However, the influenza virus ability to undergo genetic reassortment and mutation complicates vaccine strain selection and formulation. Current vaccine development strategies primarily rely on surveillance data to identify prevalent strains and predict their antigenic characteristics for inclusion in seasonal vaccines. This study aims at exploring the existing cross-reactivity among the human related 6 HA and 2 NA influenza proteins, in order to identify the most similar forms from a functional point of view, to extract key features to be used in the design of a potentially universal vaccine.


Model Development
Alexander Kulesza, Axel Loewe, Andrea Stenti, Chiara Nicolò, Enrique Morales-Orcajo, Eulalie Courcelles, Fianne Sips, Francesco Pappalardo, Giulia Russo, Marc Horner,et al.
Springer Nature Switzerland
AbstractGood Simulation Practice implies that a computational model considered for a simulation task has also been developed according to good practice.


Model Credibility
Eulalie Courcelles, Marc Horner, Payman Afshari, Alexander Kulesza, Cristina Curreli, Cristina Vaghi, Enrique Morales-Orcajo, Francesco Pappalardo, Ghislain Maquer, Giulia Russo,et al.
Springer Nature Switzerland
AbstractThe need for a framework to justify that a model has sufficient credibility to be used as a basis for internal or external (typically regulatory) decision-making is a primary concern when using modelling and simulation (M&S) in healthcare. This chapter reviews published standards on verification, validation, and uncertainty quantification (VVUQ) as well as regulatory guidance that can be used to establish model credibility in this context, providing a potential starting point for a globally harmonised model credibility framework.


Beyond the state of the art of reverse vaccinology: predicting vaccine efficacy with the universal immune system simulator for influenza
Giulia Russo, Elena Crispino, Avisa Maleki, Valentina Di Salvatore, Filippo Stanco, and Francesco Pappalardo
Springer Science and Business Media LLC
AbstractWhen it was first introduced in 2000, reverse vaccinology was defined as an in silico approach that begins with the pathogen's genomic sequence. It concludes with a list of potential proteins with a possible, but not necessarily, list of peptide candidates that need to be experimentally confirmed for vaccine production. During the subsequent years, reverse vaccinology has dramatically changed: now it consists of a large number of bioinformatics tools and processes, namely subtractive proteomics, computational vaccinology, immunoinformatics, and in silico related procedures. However, the state of the art of reverse vaccinology still misses the ability to predict the efficacy of the proposed vaccine formulation. Here, we describe how to fill the gap by introducing an advanced immune system simulator that tests the efficacy of a vaccine formulation against the disease for which it has been designed. As a working example, we entirely apply this advanced reverse vaccinology approach to design and predict the efficacy of a potential vaccine formulation against influenza H5N1. Climate change and melting glaciers are critical due to reactivating frozen viruses and emerging new pandemics. H5N1 is one of the potential strains present in icy lakes that can raise a pandemic. Investigating structural antigen protein is the most profitable therapeutic pipeline to generate an effective vaccine against H5N1. In particular, we designed a multi-epitope vaccine based on predicted epitopes of hemagglutinin and neuraminidase proteins that potentially trigger B-cells, CD4, and CD8 T-cell immune responses. Antigenicity and toxicity of all predicted CTL, Helper T-lymphocytes, and B-cells epitopes were evaluated, and both antigenic and non-allergenic epitopes were selected. From the perspective of advanced reverse vaccinology, the Universal Immune System Simulator, an in silico trial computational framework, was applied to estimate vaccine efficacy using a cohort of 100 digital patients.


Boosting multiple sclerosis lesion segmentation through attention mechanism
Alessia Rondinella, Elena Crispino, Francesco Guarnera, Oliver Giudice, Alessandro Ortis, Giulia Russo, Clara Di Lorenzo, Davide Maimone, Francesco Pappalardo, and Sebastiano Battiato
Elsevier BV






































Computational identification of differentially-expressed genes as suggested novel COVID-19 biomarkers: A bioinformatics analysis of expression profiles
Valentina Di Salvatore, Elena Crispino, Avisa Maleki, Giulia Nicotra, Giulia Russo, and Francesco Pappalardo
Elsevier BV



Reverse Vaccinology for Influenza A Virus: From Genome Sequencing to Vaccine Design
Valentina Di Salvatore, Giulia Russo, and Francesco Pappalardo
Springer US



Moving forward through the in silico modeling of multiple sclerosis: Treatment layer implementation and validation
Avisa Maleki, Elena Crispino, Serena Anna Italia, Valentina Di Salvatore, Maria Assunta Chiacchio, Fianne Sips, Roberta Bursi, Giulia Russo, Davide Maimone, and Francesco Pappalardo
Elsevier BV

RECENT SCHOLAR PUBLICATIONS

In vitro approaches to investigate the effect of chemicals on antibody production: the case study of PFASs
M Iulini, V Bettinsoli, A Maddalon, V Galbiati, AWF Janssen, K Beekmann, ...
Archives of Toxicology, 1-12 2025



Four months daily rifampicin versus three months daily rifampicin/isoniazid for the treatment of tuberculosis infection in asylum seekers: a randomized controlled trial
A Matteelli, G Russo, L Rossi, C Cerini, C Cimaglia, B Formenti, ...
Clinical Microbiology and Infection 2025



Comment on" A decade of thermostatted kinetic theory models for complex active matter living systems" by Carlo Bianca
F Pappalardo, G Russo
Physics of Life Reviews 52, 61-62 2025



Evaluating the efficacy of whole genome sequencing in predicting susceptibility profiles for first-line antituberculosis drugs
A Ghodousi, M Omrani, S Torri, H Teymouri, G Russo, C Vismara, ...
Clinical Microbiology and Infection 31 (1), 121. e1-121. e5 2025



A Head-to-Head Evaluation of a Novel Universal Influenza Vaccine Against Current Formulation: Implications for Future Immunization Strategies
V Di Salvatore, E Crispino, A Maleki, G Russo, F Pappalardo
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024



Application of PBK models for long-chain PFAS to short-chain PFAS: a proposal for toxicokinetic evaluation and in vitro to in vivo extrapolation
M Iulini, G Russo, E Crispino, E Corsini, F Pappalardo, A Paini
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024



Agent-Based Modeling of Cutaneous Lupus Erythematosus: Exploring Keratinocyte-Driven Mechanisms
A Wahab, G Russo, F Pappalardo
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024



Predicting Skin Sensitizer Potency and Immune Response Using UISS-TOX: A Novel Approach for Assessing Allergenic Potential
E Crispino, G Russo, E Arcidiacono, S Casati, E Corsini, A Worth, ...
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024



Unintended Risks of mRNA COVID-19 Vaccines: A UISS Simulation Study on Immune and Organ Health
V Di Salvatore, G Russo, F Pappalardo
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024



Advancing PFAS risk assessment: Integrative approaches using agent-based modelling and physiologically-based kinetic for environmental and health safety
M Iulini, G Russo, E Crispino, A Paini, S Fragki, E Corsini, F Pappalardo
Computational and Structural Biotechnology Journal 23, 2763-2778 2024



ICPR 2024 Competition on Multiple Sclerosis Lesion Segmentation—Methods and Results
A Rondinella, F Guarnera, E Crispino, G Russo, C Di Lorenzo, ...
International Conference on Pattern Recognition, 1-16 2024



Cascade of care for TB infection in persons newly diagnosed with HIV in Italy
A Matteelli, B Formenti, C Cimaglia, M Visconti, G di Rosario, G Russo, ...
The international journal of tuberculosis and lung disease: the official 2024



Pioneering bioinformatics with agent-based modelling: an innovative protocol to accurately forecast skin or respiratory allergic reactions to chemical sensitizers
G Russo, E Crispino, S Casati, E Corsini, A Worth, F Pappalardo
Briefings in Bioinformatics 25 (6), bbae506 2024



Breaking the Cycle: Advancements in Universal Influenza Vaccine Design
V Di Salvatore, E Crispino, G Russo, F Pappalardo
2024 IEEE International Conference on Metrology for eXtended Reality 2024



A rare cause of respiratory distress in preterm infants: a case report of acquired subglottic cysts
L Barchi, G Russo, S Donvito, G Barbato, F Leo, E Iannella, A Ghidini, ...
Italian Journal of Pediatrics 50 (1), 216 2024



Dynamic remodeling of septin structures fine-tunes myogenic differentiation
V Ugorets, PL Mendez, D Zagrebin, G Russo, Y Kerkhoff, G Kotsaris, ...
Iscience 27 (9) 2024



A method for the prediction of greenfield 3D settlement troughs around deep excavations
M De Luca, G Russo, MV Nicotera, I Esposito
Geotechnical Engineering Challenges to Meet Current and Emerging Needs of 2024



EFSA Project on the use of NAMs to explore the immunotoxicity of PFAS
E Corsini, M Iulini, V Galbiati, A Maddalon, F Pappalardo, G Russo, ...
EFSA Supporting Publications 21 (8), 8926E 2024



Model Development
A Kulesza, A Loewe, A Stenti, C Nicol, E Morales-Orcajo, E Courcelles, ...
Toward Good Simulation Practice: Best Practices for the Use of Computational 2024



Model Credibility
E Courcelles, M Horner, P Afshari, A Kulesza, C Curreli, C Vaghi, ...
Toward Good Simulation Practice: Best Practices for the Use of Computational 2024

MOST CITED SCHOLAR PUBLICATIONS

In silico clinical trials: concepts and early adoptions
F Pappalardo, G Russo, FM Tshinanu, M Viceconti
Briefings in bioinformatics 20 (5), 1699-1708 2019
Citations: 256



First evaluation of QuantiFERON-TB Gold Plus performance in contact screening
L Barcellini, E Borroni, J Brown, E Brunetti, D Campisi, PF Castellotti, ...
European respiratory journal 48 (5), 1411-1419 2016
Citations: 182



Mutations in disordered regions can cause disease by creating dileucine motifs
K Meyer, M Kirchner, B Uyar, JY Cheng, G Russo, ...
Cell 175 (1), 239-253. e17 2018
Citations: 142



Agent‐based modeling of the immune system: NetLogo, a promising framework
F Chiacchio, M Pennisi, G Russo, S Motta, F Pappalardo
BioMed research international 2014 (1), 907171 2014
Citations: 140



Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility
FT Musuamba, I Skottheim Rusten, R Lesage, G Russo, R Bursi, L Emili, ...
CPT: Pharmacometrics & Systems Pharmacology 10 (8), 804-825 2021
Citations: 102



Modeling biology spanning different scales: an open challenge
F Castiglione, F Pappalardo, C Bianca, G Russo, S Motta
BioMed research international 2014 (1), 902545 2014
Citations: 81



Computational modeling of PI3K/AKT and MAPK signaling pathways in melanoma cancer
F Pappalardo, G Russo, S Candido, M Pennisi, S Cavalieri, S Motta, ...
PLoS One 11 (3), e0152104 2016
Citations: 74



Immune-checkpoint inhibitors from cancer to COVID-19: A promising avenue for the treatment of patients with COVID-19
S Vivarelli, L Falzone, F Torino, G Scandurra, G Russo, R Bordonaro, ...
International journal of oncology 58 (2), 145-157 2020
Citations: 71



The combination of artificial intelligence and systems biology for intelligent vaccine design
G Russo, P Reche, M Pennisi, F Pappalardo
Expert Opinion on Drug Discovery 15 (11), 1267-1281 2020
Citations: 69



Credibility of In Silico Trial Technologies—A Theoretical Framing
M Viceconti, MA Jurez, C Curreli, M Pennisi, G Russo, F Pappalardo
IEEE journal of biomedical and health informatics 24 (1), 4-13 2019
Citations: 62



Wild boars’ social structure in the Mediterranean habitat
V Maselli, D Rippa, G Russo, R Ligrone, O Soppelsa, B D’Aniello, P Raia, ...
Italian Journal of Zoology 81 (4), 610-617 2014
Citations: 60



In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform
G Russo, M Pennisi, E Fichera, S Motta, G Raciti, M Viceconti, ...
BMC bioinformatics 21, 1-16 2020
Citations: 52



In silico design of recombinant multi-epitope vaccine against influenza A virus
A Maleki, G Russo, GA Parasiliti Palumbo, F Pappalardo
BMC bioinformatics 22 (Suppl 14), 617 2021
Citations: 48



A computational model to predict the immune system activation by citrus-derived vaccine adjuvants
F Pappalardo, E Fichera, N Paparone, A Lombardo, M Pennisi, G Russo, ...
Bioinformatics 32 (17), 2672-2680 2016
Citations: 47



Computational modelling approaches to vaccinology
F Pappalardo, D Flower, G Russo, M Pennisi, S Motta
Pharmacological research 92, 40-45 2015
Citations: 47



Possible Contexts of Use for In Silico Trials Methodologies: A Consensus-Based Review
M Viceconti, L Emili, P Afshari, E Courcelles, C Curreli, N Famaey, L Geris, ...
IEEE Journal of Biomedical and Health Informatics 25 (10), 3977-3982 2021
Citations: 42



The potential of computational modeling to predict disease course and treatment response in patients with relapsing multiple sclerosis
F Pappalardo, G Russo, M Pennisi, GA Parasiliti Palumbo, G Sgroi, ...
Cells 9 (3), 586 2020
Citations: 42



Examining the pre-war health burden of Ukraine for prioritisation by European countries receiving Ukrainian refugees
V Marchese, B Formenti, N Cocco, G Russo, J Testa, F Castelli, ...
The Lancet Regional Health–Europe 15 2022
Citations: 38



Computational modeling of the expansion of human cord blood CD133⁺ hematopoietic stem/progenitor cells with different cytokine combinations
F Gullo, M Van Der Garde, G Russo, M Pennisi, S Motta, F Pappalardo, ...
Bioinformatics 31 (15), 2514-2522 2015
Citations: 38



Septins as modulators of endo-lysosomal membrane traffic
K Song, G Russo, M Krauss
Frontiers in cell and developmental biology 4, 124 2016
Citations: 37