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: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
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
Background: Endomyocardial biopsy (EMB) is currently considered the gold standard for diagnosing cardiac allograft rejection. However, significant limitations related to histological interpretation variability are well-recognized. We sought to develop a methodology to evaluate EMB solely based on gene expression, without relying on histology interpretation.
Methods: Sixty-four EMBs were obtained from 47 post-heart transplant recipients, who were evaluated for allograft rejection. EMBs were subjected to mRNA sequencing, in which an unsupervised classification algorithm was used to identify the molecular signatures that best classified the EMBs. Cytokine and natriuretic peptide peripheral blood profiling was also performed. Subsequently, we performed gene network analysis to identify the gene modules and gene ontology to understand their biological relevance. We correlated our findings with the unsupervised and histological classifications.
Results: Our algorithm classifies EMBs into three categories based solely on clusters of gene expression: unsupervised classes 1, 2, and 3. Unsupervised and histological classifications were closely related, with stronger gene module-phenotype correlations for the unsupervised classes. Gene ontology enrichment analysis revealed processes impacting on the regulation of cardiac and mitochondrial function, immune response, and tissue injury response. Significant levels of cytokines and natriuretic peptides were detected following the unsupervised classification.
Conclusion: We have developed an unsupervised algorithm that classifies EMBs into three distinct categories, without relying on histology interpretation. These categories were highly correlated with mitochondrial, immune, and tissue injury response. Significant cytokine and natriuretic peptide levels were detected within the unsupervised classification. If further validated, the unsupervised classification could offer a more objective EMB evaluation.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10524567 | PMC |
http://dx.doi.org/10.1111/ctr.15011 | DOI Listing |
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