Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
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Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3098
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
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Function: require_once
Severity: Warning
Message: Attempt to read property "Count" on bool
Filename: helpers/my_audit_helper.php
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File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3100
Function: _error_handler
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
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Function: require_once
Supervised machine learning (ML) methods have been used to predict antibody responses elicited by COVID-19 vaccines in a variety of clinical settings. Here, we explored the reliability of a ML approach to predict the presence of detectable neutralizing antibody responses (NtAb) against Omicron BA.2 and BA.4/5 sublineages in the general population. Anti-SARS-CoV-2 receptor-binding domain (RBD) total antibodies were measured by the Elecsys® Anti-SARS-CoV-2 S assay (Roche Diagnostics) in all participants. NtAbs against Omicron BA.2 and BA4/5 were measured using a SARS-CoV-2 S pseudotyped neutralization assay in 100 randomly selected sera. A ML model was built using the variables of age, vaccination (number of doses) and SARS-CoV-2 infection status. The model was trained in a cohort (TC) comprising 931 participants and validated in an external cohort (VC) including 787 individuals. Receiver operating characteristics analysis indicated that an anti-SARS-CoV-2 RBD total antibody threshold of 2300 BAU/mL best discriminated between participants either exhibiting or not detectable Omicron BA.2 and Omicron BA.4/5-Spike targeted NtAb responses (87% and 84% precision, respectively). The ML model correctly classified 88% (793/901) of participants in the TC: 717/749 (95.7%) of those displaying ≥2300 BAU/mL and 76/152 (50%) of those exhibiting antibody levels <2300 BAU/mL. The model performed better in vaccinated participants, either with or without prior SARS-CoV-2 infection. The overall accuracy of the ML model in the VC was comparable. Our ML model, based upon a few easily collected parameters for predicting neutralizing activity against Omicron BA.2 and BA.4/5 (sub)variants circumvents the need to perform not only neutralization assays, but also anti-S serological tests, thus potentially saving costs in the setting of large seroprevalence studies.
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Source |
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http://dx.doi.org/10.1002/jmv.28739 | DOI Listing |
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