Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Predicting the evolution of ascending aortic aneurysm (AscAA) growth is a challenge, complicated by the intricate interplay of aortic geometry, tissue behavior, and blood flow dynamics. We investigate a flow-structural growth and remodeling (FSG) model based on the homogenized constrained mixture theory to simulate realistic AscAA growth evolution. Our approach involves initiating a finite element model with an initial elastin insult, driven by the distribution of Time-Averaged Wall Shear Stress (TAWSS) derived from computational fluid dynamics simulations. Through FSG simulation, we first calibrate the growth and remodeling material parameters to reproduce the growth observed on a patient-specific case. Then, we explore the influence of two critical parameters: the direction of the inlet jet flow, which affects the zone of significant TAWSS, and prestretch, which impacts the tissue homeostatic state. Our results show that calibrating material parameters, inlet flow direction, and prestretch allows to reproduce the observed growth, and that prestretch calibration and inlet flow direction significantly influence the simulated growth pattern. Our workflow can be applied to additional patient cases to confirm these tendencies and progress toward a predictive tool for clinical decision support.
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Source |
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http://dx.doi.org/10.1007/s10237-024-01915-6 | DOI Listing |
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