Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination.
Identifieur interne : 000507 ( PubMed/Checkpoint ); précédent : 000506; suivant : 000508Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination.
Auteurs : Karsten Jürchott [Allemagne] ; Axel Ronald Schulz [Allemagne] ; Cecilia Bozzetti [Allemagne] ; Dominika Pohlmann [Allemagne] ; Ulrik Stervbo [Allemagne] ; Sarah Warth [Allemagne] ; Julia Nora M Lzer [Allemagne] ; Julian Waldner [Allemagne] ; Brunhilde Schweiger [Allemagne] ; Sven Olek [Allemagne] ; Andreas Grützkau [Allemagne] ; Nina Babel [Allemagne] ; Andreas Thiel [Allemagne] ; Avidan Uriel Neumann [Allemagne]Source :
- PloS one [ 1932-6203 ] ; 2016.
Descripteurs français
- KwdFr :
- Activation des lymphocytes, Adulte, Adulte d'âge moyen, Allemagne, Facteurs de l'âge, Grippe humaine (), Grippe humaine (immunologie), Humains, Immunité innée, Jeune adulte, Modèles statistiques, Numération des lymphocytes, Projets pilotes, Saisons, Simulation numérique, Sous-populations de lymphocytes T (immunologie), Sous-populations de lymphocytes T (métabolisme), Sous-type H1N1 du virus de la grippe A (immunologie), Sujet âgé, Vaccination, Vaccins antigrippaux (immunologie).
- MESH :
- immunologie : Grippe humaine, Sous-populations de lymphocytes T, Sous-type H1N1 du virus de la grippe A, Vaccins antigrippaux.
- métabolisme : Sous-populations de lymphocytes T.
- Activation des lymphocytes, Adulte, Adulte d'âge moyen, Allemagne, Facteurs de l'âge, Grippe humaine, Humains, Immunité innée, Jeune adulte, Modèles statistiques, Numération des lymphocytes, Projets pilotes, Saisons, Simulation numérique, Sujet âgé, Vaccination.
- Wicri :
- geographic : Allemagne.
English descriptors
- KwdEn :
- Adult, Age Factors, Aged, Computer Simulation, Germany, Humans, Immunity, Innate, Influenza A Virus, H1N1 Subtype (immunology), Influenza Vaccines (immunology), Influenza, Human (immunology), Influenza, Human (prevention & control), Lymphocyte Activation, Lymphocyte Count, Middle Aged, Models, Statistical, Pilot Projects, Seasons, T-Lymphocyte Subsets (immunology), T-Lymphocyte Subsets (metabolism), Vaccination, Young Adult.
- MESH :
- chemical , immunology : Influenza Vaccines.
- geographic : Germany.
- immunology : Influenza A Virus, H1N1 Subtype, Influenza, Human, T-Lymphocyte Subsets.
- metabolism : T-Lymphocyte Subsets.
- prevention & control : Influenza, Human.
- Adult, Age Factors, Aged, Computer Simulation, Humans, Immunity, Innate, Lymphocyte Activation, Lymphocyte Count, Middle Aged, Models, Statistical, Pilot Projects, Seasons, Vaccination, Young Adult.
Abstract
Understanding the immune response after vaccination against new influenza strains is highly important in case of an imminent influenza pandemic and for optimization of seasonal vaccination strategies in high risk population groups, especially the elderly. Models predicting the best sero-conversion response among the three strains in the seasonal vaccine were recently suggested. However, these models use a large number of variables and/or information post- vaccination. Here in an exploratory pilot study, we analyzed the baseline immune status in young (<31 years, N = 17) versus elderly (≥50 years, N = 20) donors sero-negative to the newly emerged A(H1N1)pdm09 influenza virus strain and correlated it with the serological response to that specific strain after seasonal influenza vaccination. Extensive multi-chromatic FACS analysis (36 lymphocyte sub-populations measured) was used to quantitatively assess the cellular immune status before vaccination. We identified CD4+ T cells, and amongst them particularly naive CD4+ T cells, as the best correlates for a successful A(H1N1)pdm09 immune response. Moreover, the number of influenza strains a donor was sero-negative to at baseline (NSSN) in addition to age, as expected, were important predictive factors. Age, NSSN and CD4+ T cell count at baseline together predicted sero-protection (HAI≥40) to A(H1N1)pdm09 with a high accuracy of 89% (p-value = 0.00002). An additional validation study (N = 43 vaccinees sero-negative to A(H1N1)pdm09) has confirmed the predictive value of age, NSSN and baseline CD4+ counts (accuracy = 85%, p-value = 0.0000004). Furthermore, the inclusion of donors at ages 31-50 had shown that the age predictive function is not linear with age but rather a sigmoid with a midpoint at about 50 years. Using these results we suggest a clinically relevant prediction model that gives the probability for non-protection to A(H1N1)pdm09 influenza strain after seasonal multi-valent vaccination as a continuous function of age, NSSN and baseline CD4 count.
DOI: 10.1371/journal.pone.0150812
PubMed: 26954292
Affiliations:
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pubmed:26954292Le document en format XML
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<author><name sortKey="Warth, Sarah" sort="Warth, Sarah" uniqKey="Warth S" first="Sarah" last="Warth">Sarah Warth</name>
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<author><name sortKey="M Lzer, Julia Nora" sort="M Lzer, Julia Nora" uniqKey="M Lzer J" first="Julia Nora" last="M Lzer">Julia Nora M Lzer</name>
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<country xml:lang="fr">Allemagne</country>
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<author><name sortKey="Waldner, Julian" sort="Waldner, Julian" uniqKey="Waldner J" first="Julian" last="Waldner">Julian Waldner</name>
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<country xml:lang="fr">Allemagne</country>
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<author><name sortKey="Schweiger, Brunhilde" sort="Schweiger, Brunhilde" uniqKey="Schweiger B" first="Brunhilde" last="Schweiger">Brunhilde Schweiger</name>
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<author><name sortKey="Olek, Sven" sort="Olek, Sven" uniqKey="Olek S" first="Sven" last="Olek">Sven Olek</name>
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<country xml:lang="fr">Allemagne</country>
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<placeName><region type="land" nuts="3">Berlin</region>
<settlement type="city">Berlin</settlement>
</placeName>
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<author><name sortKey="Grutzkau, Andreas" sort="Grutzkau, Andreas" uniqKey="Grutzkau A" first="Andreas" last="Grützkau">Andreas Grützkau</name>
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<country xml:lang="fr">Allemagne</country>
<wicri:regionArea>German Rheumatism Research Centre (DRFZ), Berlin</wicri:regionArea>
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<author><name sortKey="Babel, Nina" sort="Babel, Nina" uniqKey="Babel N" first="Nina" last="Babel">Nina Babel</name>
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<country xml:lang="fr">Allemagne</country>
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<placeName><region type="land" nuts="3">Berlin</region>
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<author><name sortKey="Thiel, Andreas" sort="Thiel, Andreas" uniqKey="Thiel A" first="Andreas" last="Thiel">Andreas Thiel</name>
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<author><name sortKey="Neumann, Avidan Uriel" sort="Neumann, Avidan Uriel" uniqKey="Neumann A" first="Avidan Uriel" last="Neumann">Avidan Uriel Neumann</name>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult</term>
<term>Age Factors</term>
<term>Aged</term>
<term>Computer Simulation</term>
<term>Germany</term>
<term>Humans</term>
<term>Immunity, Innate</term>
<term>Influenza A Virus, H1N1 Subtype (immunology)</term>
<term>Influenza Vaccines (immunology)</term>
<term>Influenza, Human (immunology)</term>
<term>Influenza, Human (prevention & control)</term>
<term>Lymphocyte Activation</term>
<term>Lymphocyte Count</term>
<term>Middle Aged</term>
<term>Models, Statistical</term>
<term>Pilot Projects</term>
<term>Seasons</term>
<term>T-Lymphocyte Subsets (immunology)</term>
<term>T-Lymphocyte Subsets (metabolism)</term>
<term>Vaccination</term>
<term>Young Adult</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr"><term>Activation des lymphocytes</term>
<term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Allemagne</term>
<term>Facteurs de l'âge</term>
<term>Grippe humaine ()</term>
<term>Grippe humaine (immunologie)</term>
<term>Humains</term>
<term>Immunité innée</term>
<term>Jeune adulte</term>
<term>Modèles statistiques</term>
<term>Numération des lymphocytes</term>
<term>Projets pilotes</term>
<term>Saisons</term>
<term>Simulation numérique</term>
<term>Sous-populations de lymphocytes T (immunologie)</term>
<term>Sous-populations de lymphocytes T (métabolisme)</term>
<term>Sous-type H1N1 du virus de la grippe A (immunologie)</term>
<term>Sujet âgé</term>
<term>Vaccination</term>
<term>Vaccins antigrippaux (immunologie)</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="immunology" xml:lang="en"><term>Influenza Vaccines</term>
</keywords>
<keywords scheme="MESH" type="geographic" xml:lang="en"><term>Germany</term>
</keywords>
<keywords scheme="MESH" qualifier="immunologie" xml:lang="fr"><term>Grippe humaine</term>
<term>Sous-populations de lymphocytes T</term>
<term>Sous-type H1N1 du virus de la grippe A</term>
<term>Vaccins antigrippaux</term>
</keywords>
<keywords scheme="MESH" qualifier="immunology" xml:lang="en"><term>Influenza A Virus, H1N1 Subtype</term>
<term>Influenza, Human</term>
<term>T-Lymphocyte Subsets</term>
</keywords>
<keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>T-Lymphocyte Subsets</term>
</keywords>
<keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Sous-populations de lymphocytes T</term>
</keywords>
<keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Influenza, Human</term>
</keywords>
<keywords scheme="MESH" xml:lang="en"><term>Adult</term>
<term>Age Factors</term>
<term>Aged</term>
<term>Computer Simulation</term>
<term>Humans</term>
<term>Immunity, Innate</term>
<term>Lymphocyte Activation</term>
<term>Lymphocyte Count</term>
<term>Middle Aged</term>
<term>Models, Statistical</term>
<term>Pilot Projects</term>
<term>Seasons</term>
<term>Vaccination</term>
<term>Young Adult</term>
</keywords>
<keywords scheme="MESH" xml:lang="fr"><term>Activation des lymphocytes</term>
<term>Adulte</term>
<term>Adulte d'âge moyen</term>
<term>Allemagne</term>
<term>Facteurs de l'âge</term>
<term>Grippe humaine</term>
<term>Humains</term>
<term>Immunité innée</term>
<term>Jeune adulte</term>
<term>Modèles statistiques</term>
<term>Numération des lymphocytes</term>
<term>Projets pilotes</term>
<term>Saisons</term>
<term>Simulation numérique</term>
<term>Sujet âgé</term>
<term>Vaccination</term>
</keywords>
<keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Allemagne</term>
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<front><div type="abstract" xml:lang="en">Understanding the immune response after vaccination against new influenza strains is highly important in case of an imminent influenza pandemic and for optimization of seasonal vaccination strategies in high risk population groups, especially the elderly. Models predicting the best sero-conversion response among the three strains in the seasonal vaccine were recently suggested. However, these models use a large number of variables and/or information post- vaccination. Here in an exploratory pilot study, we analyzed the baseline immune status in young (<31 years, N = 17) versus elderly (≥50 years, N = 20) donors sero-negative to the newly emerged A(H1N1)pdm09 influenza virus strain and correlated it with the serological response to that specific strain after seasonal influenza vaccination. Extensive multi-chromatic FACS analysis (36 lymphocyte sub-populations measured) was used to quantitatively assess the cellular immune status before vaccination. We identified CD4+ T cells, and amongst them particularly naive CD4+ T cells, as the best correlates for a successful A(H1N1)pdm09 immune response. Moreover, the number of influenza strains a donor was sero-negative to at baseline (NSSN) in addition to age, as expected, were important predictive factors. Age, NSSN and CD4+ T cell count at baseline together predicted sero-protection (HAI≥40) to A(H1N1)pdm09 with a high accuracy of 89% (p-value = 0.00002). An additional validation study (N = 43 vaccinees sero-negative to A(H1N1)pdm09) has confirmed the predictive value of age, NSSN and baseline CD4+ counts (accuracy = 85%, p-value = 0.0000004). Furthermore, the inclusion of donors at ages 31-50 had shown that the age predictive function is not linear with age but rather a sigmoid with a midpoint at about 50 years. Using these results we suggest a clinically relevant prediction model that gives the probability for non-protection to A(H1N1)pdm09 influenza strain after seasonal multi-valent vaccination as a continuous function of age, NSSN and baseline CD4 count. </div>
</front>
</TEI>
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<DateCompleted><Year>2016</Year>
<Month>08</Month>
<Day>03</Day>
</DateCompleted>
<DateRevised><Year>2019</Year>
<Month>02</Month>
<Day>19</Day>
</DateRevised>
<Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN>
<JournalIssue CitedMedium="Internet"><Volume>11</Volume>
<Issue>3</Issue>
<PubDate><Year>2016</Year>
</PubDate>
</JournalIssue>
<Title>PloS one</Title>
<ISOAbbreviation>PLoS ONE</ISOAbbreviation>
</Journal>
<ArticleTitle>Highly Predictive Model for a Protective Immune Response to the A(H1N1)pdm2009 Influenza Strain after Seasonal Vaccination.</ArticleTitle>
<Pagination><MedlinePgn>e0150812</MedlinePgn>
</Pagination>
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<Abstract><AbstractText>Understanding the immune response after vaccination against new influenza strains is highly important in case of an imminent influenza pandemic and for optimization of seasonal vaccination strategies in high risk population groups, especially the elderly. Models predicting the best sero-conversion response among the three strains in the seasonal vaccine were recently suggested. However, these models use a large number of variables and/or information post- vaccination. Here in an exploratory pilot study, we analyzed the baseline immune status in young (<31 years, N = 17) versus elderly (≥50 years, N = 20) donors sero-negative to the newly emerged A(H1N1)pdm09 influenza virus strain and correlated it with the serological response to that specific strain after seasonal influenza vaccination. Extensive multi-chromatic FACS analysis (36 lymphocyte sub-populations measured) was used to quantitatively assess the cellular immune status before vaccination. We identified CD4+ T cells, and amongst them particularly naive CD4+ T cells, as the best correlates for a successful A(H1N1)pdm09 immune response. Moreover, the number of influenza strains a donor was sero-negative to at baseline (NSSN) in addition to age, as expected, were important predictive factors. Age, NSSN and CD4+ T cell count at baseline together predicted sero-protection (HAI≥40) to A(H1N1)pdm09 with a high accuracy of 89% (p-value = 0.00002). An additional validation study (N = 43 vaccinees sero-negative to A(H1N1)pdm09) has confirmed the predictive value of age, NSSN and baseline CD4+ counts (accuracy = 85%, p-value = 0.0000004). Furthermore, the inclusion of donors at ages 31-50 had shown that the age predictive function is not linear with age but rather a sigmoid with a midpoint at about 50 years. Using these results we suggest a clinically relevant prediction model that gives the probability for non-protection to A(H1N1)pdm09 influenza strain after seasonal multi-valent vaccination as a continuous function of age, NSSN and baseline CD4 count. </AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Jürchott</LastName>
<ForeName>Karsten</ForeName>
<Initials>K</Initials>
<AffiliationInfo><Affiliation>Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Institute for Theoretical Biology, Humboldt University, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Schulz</LastName>
<ForeName>Axel Ronald</ForeName>
<Initials>AR</Initials>
<AffiliationInfo><Affiliation>Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Bozzetti</LastName>
<ForeName>Cecilia</ForeName>
<Initials>C</Initials>
<AffiliationInfo><Affiliation>Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Pohlmann</LastName>
<ForeName>Dominika</ForeName>
<Initials>D</Initials>
<AffiliationInfo><Affiliation>Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Stervbo</LastName>
<ForeName>Ulrik</ForeName>
<Initials>U</Initials>
<AffiliationInfo><Affiliation>Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Warth</LastName>
<ForeName>Sarah</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Mälzer</LastName>
<ForeName>Julia Nora</ForeName>
<Initials>JN</Initials>
<AffiliationInfo><Affiliation>Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Waldner</LastName>
<ForeName>Julian</ForeName>
<Initials>J</Initials>
<AffiliationInfo><Affiliation>Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Schweiger</LastName>
<ForeName>Brunhilde</ForeName>
<Initials>B</Initials>
<AffiliationInfo><Affiliation>Robert Koch Institute, National Reference Center for Influenza, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Olek</LastName>
<ForeName>Sven</ForeName>
<Initials>S</Initials>
<AffiliationInfo><Affiliation>Ivana Türbachova Laboratory for Epigentics, Epiontis GmbH, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Grützkau</LastName>
<ForeName>Andreas</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>German Rheumatism Research Centre (DRFZ), Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Babel</LastName>
<ForeName>Nina</ForeName>
<Initials>N</Initials>
<AffiliationInfo><Affiliation>Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Thiel</LastName>
<ForeName>Andreas</ForeName>
<Initials>A</Initials>
<AffiliationInfo><Affiliation>Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Neumann</LastName>
<ForeName>Avidan Uriel</ForeName>
<Initials>AU</Initials>
<AffiliationInfo><Affiliation>Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Charité University Hospital, Berlin, Germany.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Institute for Theoretical Biology, Humboldt University, Berlin, Germany.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Swiss Institute for Allergy and Asthma Research (SIAF), University of Zürich, Davos, Switzerland.</Affiliation>
</AffiliationInfo>
</Author>
</AuthorList>
<Language>eng</Language>
<PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType>
<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
</PublicationTypeList>
<ArticleDate DateType="Electronic"><Year>2016</Year>
<Month>03</Month>
<Day>08</Day>
</ArticleDate>
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<MedlineTA>PLoS One</MedlineTA>
<NlmUniqueID>101285081</NlmUniqueID>
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<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000367" MajorTopicYN="N">Age Factors</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005858" MajorTopicYN="N" Type="Geographic">Germany</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D007113" MajorTopicYN="N">Immunity, Innate</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D053118" MajorTopicYN="N">Influenza A Virus, H1N1 Subtype</DescriptorName>
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</MeshHeading>
<MeshHeading><DescriptorName UI="D007252" MajorTopicYN="N">Influenza Vaccines</DescriptorName>
<QualifierName UI="Q000276" MajorTopicYN="Y">immunology</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName>
<QualifierName UI="Q000276" MajorTopicYN="Y">immunology</QualifierName>
<QualifierName UI="Q000517" MajorTopicYN="Y">prevention & control</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008213" MajorTopicYN="N">Lymphocyte Activation</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D018655" MajorTopicYN="N">Lymphocyte Count</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName>
</MeshHeading>
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</MeshHeading>
<MeshHeading><DescriptorName UI="D010865" MajorTopicYN="N">Pilot Projects</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D012621" MajorTopicYN="Y">Seasons</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D016176" MajorTopicYN="N">T-Lymphocyte Subsets</DescriptorName>
<QualifierName UI="Q000276" MajorTopicYN="N">immunology</QualifierName>
<QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D014611" MajorTopicYN="Y">Vaccination</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName>
</MeshHeading>
</MeshHeadingList>
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<Month>02</Month>
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<affiliations><list><country><li>Allemagne</li>
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<tree><country name="Allemagne"><region name="Berlin"><name sortKey="Jurchott, Karsten" sort="Jurchott, Karsten" uniqKey="Jurchott K" first="Karsten" last="Jürchott">Karsten Jürchott</name>
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<name sortKey="Babel, Nina" sort="Babel, Nina" uniqKey="Babel N" first="Nina" last="Babel">Nina Babel</name>
<name sortKey="Bozzetti, Cecilia" sort="Bozzetti, Cecilia" uniqKey="Bozzetti C" first="Cecilia" last="Bozzetti">Cecilia Bozzetti</name>
<name sortKey="Grutzkau, Andreas" sort="Grutzkau, Andreas" uniqKey="Grutzkau A" first="Andreas" last="Grützkau">Andreas Grützkau</name>
<name sortKey="M Lzer, Julia Nora" sort="M Lzer, Julia Nora" uniqKey="M Lzer J" first="Julia Nora" last="M Lzer">Julia Nora M Lzer</name>
<name sortKey="Neumann, Avidan Uriel" sort="Neumann, Avidan Uriel" uniqKey="Neumann A" first="Avidan Uriel" last="Neumann">Avidan Uriel Neumann</name>
<name sortKey="Olek, Sven" sort="Olek, Sven" uniqKey="Olek S" first="Sven" last="Olek">Sven Olek</name>
<name sortKey="Pohlmann, Dominika" sort="Pohlmann, Dominika" uniqKey="Pohlmann D" first="Dominika" last="Pohlmann">Dominika Pohlmann</name>
<name sortKey="Schulz, Axel Ronald" sort="Schulz, Axel Ronald" uniqKey="Schulz A" first="Axel Ronald" last="Schulz">Axel Ronald Schulz</name>
<name sortKey="Schweiger, Brunhilde" sort="Schweiger, Brunhilde" uniqKey="Schweiger B" first="Brunhilde" last="Schweiger">Brunhilde Schweiger</name>
<name sortKey="Stervbo, Ulrik" sort="Stervbo, Ulrik" uniqKey="Stervbo U" first="Ulrik" last="Stervbo">Ulrik Stervbo</name>
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<name sortKey="Waldner, Julian" sort="Waldner, Julian" uniqKey="Waldner J" first="Julian" last="Waldner">Julian Waldner</name>
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