In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 3 ( 2022-3-10), p. e1009910-
Abstract:
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. Here we propose a stochastic platelet deposition model and an inferential scheme to estimate the biologically meaningful model parameters using approximate Bayesian computation with a summary statistic that maximally discriminates between different types of patients. Inferred parameters from data collected on healthy volunteers and different patient types help us to identify specific biological parameters and hence biological reasoning behind the dysfunction for each type of patients. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1009910
DOI:
10.1371/journal.pcbi.1009910.g001
DOI:
10.1371/journal.pcbi.1009910.g002
DOI:
10.1371/journal.pcbi.1009910.g003
DOI:
10.1371/journal.pcbi.1009910.g004
DOI:
10.1371/journal.pcbi.1009910.g005
DOI:
10.1371/journal.pcbi.1009910.g006
DOI:
10.1371/journal.pcbi.1009910.g007
DOI:
10.1371/journal.pcbi.1009910.g008
DOI:
10.1371/journal.pcbi.1009910.t001
DOI:
10.1371/journal.pcbi.1009910.t002
DOI:
10.1371/journal.pcbi.1009910.t003
DOI:
10.1371/journal.pcbi.1009910.t004
DOI:
10.1371/journal.pcbi.1009910.s001
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
Permalink