1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
2. Όλο το Αποθετήριο
3. Μεταδιδακτορικοί ερευνητές

Τύπος:	Αναρτημένη ανακοίνωση (poster)
Τίτλος:	Comorbidities risk assessment: a predictive approach
Συγγραφέας:	[EL] Μπαλασοπούλου, Αγγελική[EN] Balasopoulou, Angeliki [ES] Gómez-Tamayo, José Carlos
Επιβλέπων διατριβής:	[EL] Κατσίλα, Θεοδώρα[EN] Katsila, Theodora
Ημερομηνία:	Νοε-2021
Περίληψη:	Comorbidities Risk Assessment: a predictive approach Angeliki Balasopoulou1#, Jose Carlos Gomez Tamayo2#, Theodora Katsila1* 1Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece 2Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain, #These authors contributed equally to this work *email:thkatsila@eie.gr Complex disease traits and clinical phenotypes stem from genetic and environmental influences (e.g. diet, viral biomolecules/stimuli). Problematic self-regulation of the inflammasome may serve as a paradigm. Inflammatory responses shift a defense mechanism into a perpetuating inflammatory response leading to disease comorbidities. Herein, we developed a predictive approach for comorbidities risk assessment that highlight molecular signatures of disease severity. An in-depth in silico analysis of disease traits and clinical phenotypes as well as tissue-specific datasets were integrated with functional data from publicly available data repositories and databases. R and Python programming languages were employed. Two strategies were implemented: agnostic analysis and targeted analysis, followed by extensive data and text mining for extra filtering of outcomes. Protein networks revealed missense variants of interest. Data and text mining were performed to account for biases. Our approach detected patterns of comorbidities as the net result of genetic and environmental influences (e.g. diet, viral biomolecules/stimuli). Ιn silico analyses resulted in 1,156 missense variants of key interest either at the orthosteric or allosteric site of key proteins. A predictive evaluation of comorbidity development risk may identify those patients at high risk and thus, empower optimum patient stratification This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Reinforcement of Postdoctoral Researchers - 2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (ΙΚΥ).
Γλώσσα:	Αγγλικά
Τόπος δημοσίευσης:	Αθήνα, Ελλάδα
Σελίδες:	7
Θεματική κατηγορία:	[EL] Ιατρική πληροφορική[EN] Medical informatics
Λέξεις-κλειδιά:	comorbities; risk assessment; predictive approaches; complex diseases
Κάτοχος πνευματικών δικαιωμάτων:	© The Author(s) 2021
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://eebmbcongress.gr/wp-content/uploads/2021/11/71EEBMB_AbstractBook.pdf
Τίτλος πηγής δημοσίευσης:	71ο Πανελλήνιο Συνέδριο της Ελληνικής Εταιρείας Βιοχημείας και Μοριακής Βιολογίας (EEBMB) Abstracts
Σελίδες τεκμηρίου (στην πηγή):	93
Όνομα εκδήλωσης:	71ο Πανελλήνιο Συνέδριο Ελληνικής Εταιρείας Βιοχημείας & Μοριακής Βιολογίας
Τοποθεσία εκδήλωσης:	Αθήνα, Ελλάδα
Ημ/νία έναρξης εκδήλωσης:	26/11/2021
Ημ/νία λήξης εκδήλωσης:	28/11/2021
Σημειώσεις:	This research is co-financed by Greece and the European Union (European Social Fund- ESF) through the Operational Programme «Human Resources Development, Education and Lifelong Learning» in the context of the project “Reinforcement of Postdoctoral Researchers - 2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (ΙΚΥ).
Εμφανίζεται στις συλλογές:	Μεταδιδακτορικοί ερευνητές