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Severe acute respiratory syndrome diagnostics using a coronavirus protein microarray.


ABSTRACT: To monitor severe acute respiratory syndrome (SARS) infection, a coronavirus protein microarray that harbors proteins from SARS coronavirus (SARS-CoV) and five additional coronaviruses was constructed. These microarrays were used to screen approximately 400 Canadian sera from the SARS outbreak, including samples from confirmed SARS-CoV cases, respiratory illness patients, and healthcare professionals. A computer algorithm that uses multiple classifiers to predict samples from SARS patients was developed and used to predict 206 sera from Chinese fever patients. The test assigned patients into two distinct groups: those with antibodies to SARS-CoV and those without. The microarray also identified patients with sera reactive against other coronavirus proteins. Our results correlated well with an indirect immunofluorescence test and demonstrated that viral infection can be monitored for many months after infection. We show that protein microarrays can serve as a rapid, sensitive, and simple tool for large-scale identification of viral-specific antibodies in sera.

SUBMITTER: Zhu H 

PROVIDER: S-EPMC1449637 | biostudies-literature | 2006 Mar

REPOSITORIES: biostudies-literature

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Severe acute respiratory syndrome diagnostics using a coronavirus protein microarray.

Zhu Heng H   Hu Shaohui S   Jona Ghil G   Zhu Xiaowei X   Kreiswirth Nate N   Willey Barbara M BM   Mazzulli Tony T   Liu Guozhen G   Song Qifeng Q   Chen Peng P   Cameron Mark M   Tyler Andrea A   Wang Jian J   Wen Jie J   Chen Weijun W   Compton Susan S   Snyder Michael M  

Proceedings of the National Academy of Sciences of the United States of America 20060307 11


To monitor severe acute respiratory syndrome (SARS) infection, a coronavirus protein microarray that harbors proteins from SARS coronavirus (SARS-CoV) and five additional coronaviruses was constructed. These microarrays were used to screen approximately 400 Canadian sera from the SARS outbreak, including samples from confirmed SARS-CoV cases, respiratory illness patients, and healthcare professionals. A computer algorithm that uses multiple classifiers to predict samples from SARS patients was d  ...[more]

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