1 Ocak 2023 Dergi makalesi Açık Erişim

Parlatan, Ugur; Ozen, Mehmet Ozgun; Kecoglu, Ibrahim; Koyuncu, Batuhan; Torun, Hulya; Khalafkhany, Davod; Loc, Irem; Ogut, Mehmet Giray; Inci, Fatih; Akin, Demir; Solaroglu, Ihsan; Ozoren, Nesrin; Unlu, Mehmet Burcin; Demirci, Utkan

DataCite XML

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  <identifier identifierType="URL">https://aperta.ulakbim.gov.tr/record/271136</identifier>
  <creators>
    <creator>
      <creatorName>Parlatan, Ugur</creatorName>
      <givenName>Ugur</givenName>
      <familyName>Parlatan</familyName>
    </creator>
    <creator>
      <creatorName>Ozen, Mehmet Ozgun</creatorName>
      <givenName>Mehmet Ozgun</givenName>
      <familyName>Ozen</familyName>
      <affiliation>Stanford Sch Med, Canary Ctr, BioAcoust MEMS Med Lab BAMM, Dept Radiol,Stanford Canc Early Detect, Palo Alto, CA 94304 USA</affiliation>
    </creator>
    <creator>
      <creatorName>Kecoglu, Ibrahim</creatorName>
      <givenName>Ibrahim</givenName>
      <familyName>Kecoglu</familyName>
      <affiliation>Bogazici Univ, Dept Phys, TR-34342 Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Koyuncu, Batuhan</creatorName>
      <givenName>Batuhan</givenName>
      <familyName>Koyuncu</familyName>
      <affiliation>Bogazici Univ, Dept Comp Engn, TR-34342 Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Torun, Hulya</creatorName>
      <givenName>Hulya</givenName>
      <familyName>Torun</familyName>
    </creator>
    <creator>
      <creatorName>Khalafkhany, Davod</creatorName>
      <givenName>Davod</givenName>
      <familyName>Khalafkhany</familyName>
      <affiliation>Bogazici Univ, Ctr Life Sci &amp; Technol, Dept Mol Biol &amp; Genet, Apoptosis &amp; Canc Immunol Lab AKiL, TR-34342 Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Loc, Irem</creatorName>
      <givenName>Irem</givenName>
      <familyName>Loc</familyName>
      <affiliation>Bogazici Univ, Dept Phys, TR-34342 Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Ogut, Mehmet Giray</creatorName>
      <givenName>Mehmet Giray</givenName>
      <familyName>Ogut</familyName>
      <affiliation>Stanford Sch Med, Canary Ctr, BioAcoust MEMS Med Lab BAMM, Dept Radiol,Stanford Canc Early Detect, Palo Alto, CA 94304 USA</affiliation>
    </creator>
    <creator>
      <creatorName>Inci, Fatih</creatorName>
      <givenName>Fatih</givenName>
      <familyName>Inci</familyName>
    </creator>
    <creator>
      <creatorName>Akin, Demir</creatorName>
      <givenName>Demir</givenName>
      <familyName>Akin</familyName>
      <affiliation>Stanford Sch Med, Canary Ctr, BioAcoust MEMS Med Lab BAMM, Dept Radiol,Stanford Canc Early Detect, Palo Alto, CA 94304 USA</affiliation>
    </creator>
    <creator>
      <creatorName>Solaroglu, Ihsan</creatorName>
      <givenName>Ihsan</givenName>
      <familyName>Solaroglu</familyName>
    </creator>
    <creator>
      <creatorName>Ozoren, Nesrin</creatorName>
      <givenName>Nesrin</givenName>
      <familyName>Ozoren</familyName>
      <affiliation>Bogazici Univ, Ctr Life Sci &amp; Technol, Dept Mol Biol &amp; Genet, Apoptosis &amp; Canc Immunol Lab AKiL, TR-34342 Istanbul, Turkiye</affiliation>
    </creator>
    <creator>
      <creatorName>Unlu, Mehmet Burcin</creatorName>
      <givenName>Mehmet Burcin</givenName>
      <familyName>Unlu</familyName>
    </creator>
    <creator>
      <creatorName>Demirci, Utkan</creatorName>
      <givenName>Utkan</givenName>
      <familyName>Demirci</familyName>
      <affiliation>Stanford Sch Med, Canary Ctr, BioAcoust MEMS Med Lab BAMM, Dept Radiol,Stanford Canc Early Detect, Palo Alto, CA 94304 USA</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Label-Free Identification Of Exosomes Using Raman Spectroscopy And Machine Learning</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2023</publicationYear>
  <dates>
    <date dateType="Issued">2023-01-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/271136</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1002/smll.202205519</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.&lt;/p&gt;</description>
  </descriptions>
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