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
The analysis of DNA methylation (DNAm) levels at specific CpG sites represents one of the most promising molecular techniques for estimating an individual's age. To date, a considerable number of studies have reported the development of age prediction models on the basis of DNAm in body fluids, with only a few utilizing buccal swabs. The objective of this study was to identify age-dependent methylation CpG sites in three different genes (HOXC4, TRIM59, and ELOVL2) in buccal swab samples from the Chinese Han population. A total of 461 buccal swabs, with an age range of 0.4-80.8 years, were divided into a training set (n = 325) and a validation set (n = 136). Samples were analyzed by pyrosequencing in order to identify age-related genes with correlation coefficient. A random forest regression model was ultimately proposed, including eight CpGs in three genes, with a mean absolute error (MAE) of 2.119 years. The model performs independent validation set with an MAE of 4.391 years. Our findings illustrate that buccal swabs present a suitable alternative to biological traces for age prediction based on DNAm pattern using pyrosequencing and random forest regression, offering the additional advantage of being collected noninvasively.
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Source |
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http://dx.doi.org/10.1002/elps.202400075 | DOI Listing |
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