Identification of scrapie infection from blood serum by Fourier transform infrared spectroscopy.
Identifieur interne : 000798 ( PubMed/Corpus ); précédent : 000797; suivant : 000799Identification of scrapie infection from blood serum by Fourier transform infrared spectroscopy.
Auteurs : Jürgen Schmitt ; Michael Beekes ; Angelika Brauer ; Thomas Udelhoven ; Peter Lasch ; Dieter NaumannSource :
- Analytical chemistry [ 0003-2700 ] ; 2002.
English descriptors
- KwdEn :
- MESH :
- blood : Scrapie.
- diagnosis : Scrapie.
- methods : Spectroscopy, Fourier Transform Infrared.
- Animals, Artificial Intelligence, Blood, Cricetinae, Female, Male, Neural Networks (Computer), Predictive Value of Tests, Sensitivity and Specificity.
Abstract
We describe a new Fourier transform infrared (FT-IR) spectroscopy-based diagnostic approach, which may provide for the first time a rapid, reliable, and inexpensive blood test for scrapie and related transmissible spongiform encephalopathies (TSE). Blood serum from 146 terminally ill Syrian hamsters infected with 263K scrapie via different routes of inoculation and from 166 healthy control animals was analyzed by FT-IR spectroscopy and artificial neural networks (ANN). This revealed characteristic molecular alterations in the serum of infected donors. Different ANN models were constructed and challenged with previously unknown samples in test runs in order to establish whether the new method is able to discriminate between normal and infected animals. Optimized ANNs consistently yielded test sensitivities and specificities of 97% and 100%, respectively. The predictive value of a positive (negative) test was 100% (98%). The proposed serum test circumvents substantial drawbacks of conventional TSE diagnostics and can be fully automated.
PubMed: 12175177
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Blood serum from 146 terminally ill Syrian hamsters infected with 263K scrapie via different routes of inoculation and from 166 healthy control animals was analyzed by FT-IR spectroscopy and artificial neural networks (ANN). This revealed characteristic molecular alterations in the serum of infected donors. Different ANN models were constructed and challenged with previously unknown samples in test runs in order to establish whether the new method is able to discriminate between normal and infected animals. Optimized ANNs consistently yielded test sensitivities and specificities of 97% and 100%, respectively. The predictive value of a positive (negative) test was 100% (98%). The proposed serum test circumvents substantial drawbacks of conventional TSE diagnostics and can be fully automated.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">12175177</PMID><DateCreated><Year>2002</Year><Month>08</Month><Day>14</Day></DateCreated><DateCompleted><Year>2003</Year><Month>07</Month><Day>25</Day></DateCompleted><DateRevised><Year>2005</Year><Month>11</Month><Day>17</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0003-2700</ISSN><JournalIssue CitedMedium="Print"><Volume>74</Volume><Issue>15</Issue><PubDate><Year>2002</Year><Month>Aug</Month><Day>01</Day></PubDate></JournalIssue><Title>Analytical chemistry</Title><ISOAbbreviation>Anal. Chem.</ISOAbbreviation></Journal><ArticleTitle>Identification of scrapie infection from blood serum by Fourier transform infrared spectroscopy.</ArticleTitle><Pagination><MedlinePgn>3865-8</MedlinePgn></Pagination><Abstract><AbstractText>We describe a new Fourier transform infrared (FT-IR) spectroscopy-based diagnostic approach, which may provide for the first time a rapid, reliable, and inexpensive blood test for scrapie and related transmissible spongiform encephalopathies (TSE). Blood serum from 146 terminally ill Syrian hamsters infected with 263K scrapie via different routes of inoculation and from 166 healthy control animals was analyzed by FT-IR spectroscopy and artificial neural networks (ANN). This revealed characteristic molecular alterations in the serum of infected donors. Different ANN models were constructed and challenged with previously unknown samples in test runs in order to establish whether the new method is able to discriminate between normal and infected animals. Optimized ANNs consistently yielded test sensitivities and specificities of 97% and 100%, respectively. The predictive value of a positive (negative) test was 100% (98%). The proposed serum test circumvents substantial drawbacks of conventional TSE diagnostics and can be fully automated.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Schmitt</LastName><ForeName>Jürgen</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>FB VI, Spectroscopy Group, University of Trier, Universitätsring, Germany.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Beekes</LastName><ForeName>Michael</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Brauer</LastName><ForeName>Angelika</ForeName><Initials>A</Initials></Author><Author ValidYN="Y"><LastName>Udelhoven</LastName><ForeName>Thomas</ForeName><Initials>T</Initials></Author><Author ValidYN="Y"><LastName>Lasch</LastName><ForeName>Peter</ForeName><Initials>P</Initials></Author><Author ValidYN="Y"><LastName>Naumann</LastName><ForeName>Dieter</ForeName><Initials>D</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Anal Chem</MedlineTA><NlmUniqueID>0370536</NlmUniqueID><ISSNLinking>0003-2700</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001185" MajorTopicYN="N">Artificial Intelligence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001769" MajorTopicYN="Y">Blood</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006224" MajorTopicYN="N">Cricetinae</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016571" MajorTopicYN="N">Neural Networks (Computer)</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011237" MajorTopicYN="N">Predictive Value of Tests</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D012608" MajorTopicYN="N">Scrapie</DescriptorName><QualifierName UI="Q000097" MajorTopicYN="Y">blood</QualifierName><QualifierName UI="Q000175" MajorTopicYN="Y">diagnosis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D012680" MajorTopicYN="N">Sensitivity and Specificity</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017550" MajorTopicYN="N">Spectroscopy, Fourier Transform Infrared</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2002</Year><Month>8</Month><Day>15</Day><Hour>10</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2003</Year><Month>7</Month><Day>26</Day><Hour>5</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2002</Year><Month>8</Month><Day>15</Day><Hour>10</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">12175177</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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