Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1075
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3195
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Cardiovascular diseases remain a leading cause of mortality in the United States, driving the need for advanced cardiovascular devices and pharmaceuticals. Mock Circulatory Loops (MCLs) have emerged as essential tools for in vitro testing, replicating pulsatile pressure and flow to simulate various physiological and pathological conditions. While many studies focus on custom MCL designs tailored to specific applications, few have systematically reviewed their use in device testing, and none have assessed their broader utility across diverse biomedical domains. This comprehensive review categorizes MCL designs into three types: mechanical, computational, and hybrid. Applications are classified into four major areas: Cardiovascular Devices Testing, Clinical Training and Education, Hemodynamics and Blood Flow Studies, and Disease Modeling. Most existing MCLs are complex, highly specialized, and difficult to reproduce, highlighting the need for simplified, standardized, and programmable hybrid systems. Improved validation and waveform fidelity-particularly through incorporation of the dicrotic notch and other waveform parameters-are critical for advancing MCL reliability. Furthermore, integration of machine learning and artificial intelligence holds significant promise for enhancing waveform analysis, diagnostics, predictive modeling, and personalized care. In conclusion, the development of MCLs should prioritize standardization, simplification, and broader accessibility to expand their impact across biomedical research and clinical translation.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12383638 | PMC |
http://dx.doi.org/10.3390/bioengineering12080851 | DOI Listing |