arXiv subjects

Maria Luisa Chiusano

Publications and source records attributed to Maria Luisa Chiusano.

3 recordsLinked to original sources

arXiv2022

EOSC-LIFE WP4 TOOLBOX: Toolbox for sharing of sensitive data -- a concept description

The Horizon 2020 project EOSC-Life brings together the 13 Life Science 'ESFRI' research infrastructures to create an open, digital and collaborative space for biological and medical research. Sharing sensitive data is a specific challenge within EOSC-Life. For that reason, a toolbox is being developed, providing information to researchers who wish to share and/or use sensitive data in a cloud environment in general, and the European Open Science Cloud in particular. The sensitivity of the data may arise from its personal nature but can also be caused by intellectual property considerations, biohazard concerns, or the Nagoya protocol. The toolbox will not create new content, instead, it will allow researchers to find existing resources that are relevant for sharing sensitive data across all participating research infrastructures (F in FAIR). The toolbox will provide links to recommendations, procedures, and best practices, as well as to software (tools) to support data sharing and reuse. It will be based upon a tagging (categorisation) system, allowing consistent labelling and categorisation of resources. The current design document provides an outline for the anticipated toolbox, as well as its basic principles regarding content and sustainability.

q-bio.OT↗

arXiv2021

Variant-driven multi-wave pattern of COVID-19 via a Machine Learning analysis of spike protein mutations

Applying a ML approach to the temporal variability of the Spike protein sequence enables us to identify, classify and track emerging virus variants. Our analysis is unbiased, in the sense that it does not require any prior knowledge of the variant characteristics, and our results are validated by other informed methods that define variants based on the complete genome. Furthermore, correlating persistent variants of our approach to epidemiological data, we discover that each new wave of the COVID-19 pandemic is driven and dominated by a new emerging variant. Our results are therefore indispensable for further studies on the evolution of SARS-CoV-2 and the prediction of evolutionary patterns that determine current and future mutations of the Spike proteins, as well as their diversification and persistence during the viral spread. Moreover, our ML algorithm works as an efficient early warning system for the emergence of new persistent variants that may pose a threat of triggering a new wave of COVID-19. Capable of a timely identification of potential new epidemiological threats when the variant only represents 1% of the new sequences, our ML strategy is a crucial tool for decision makers to define short and long term strategies to curb future outbreaks. The same methodology can be applied to other viral diseases, influenza included, if sufficient sequencing data is available.

q-bio.GN↗

arXiv2020

The modelling of COVID19 pathways sheds light on mechanisms, opportunities and on controversial interpretations of medical treatments. v2

The new coronavirus (2019-nCoV or SARS-CoV2), inducing the current pandemic disease (COVID-19) and causing pneumoniae in humans, is dramatically increasing in epidemic scale since its first appearance in Wuhan, China, in December 2019. The first infection from epidemic coronaviruses in 2003 fostered the spread of an overwhelming amount of related scientific efforts. The manifold aspects that have been raised, as well as their redundancy offer precious information that has been underexploited and needs to be critically re-evaluated, appropriately used and offered to the whole community, from scientists, to medical doctors, stakeholders and common people. These efforts will favour a holistic view on the comprehension, prevention and development of strategies (pharmacological, clinical etc) as well as common intervention against the new coronavirus spreading. Here we describe a model that emerged from our analysis that was focused on the Renin Angiotensin System (RAS) and the possible routes linking it to the viral infection. because the infection is mediated by the viral receptor on human cell membranes Angiotensin Converting Enzyme (ACE2), which is a key component in RAS signalling. The model depicts the main pathways determining the disease and the molecular framework for its establishment, and can help to shed light on mechanisms involved in the infection. It promptly gives an answer to some of the controversial, and still open, issues concerning predisposing conditions and medical treatments that protect from or favour the severity of the disease (such as the use of ACE inhibitors or ARBs/sartans), or to the sex related biases in the affected population. The model highlights novel opportunities for further investigations, diagnosis and appropriate intervention to understand and fight COVID19.

q-bio.MN↗