Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis
Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggeste...
Saved in:
| Published in | Frontiers in microbiology Vol. 12; p. 711134 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Media
23.12.2021
Frontiers Media S.A |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1664-302X 1664-302X |
| DOI | 10.3389/fmicb.2021.711134 |
Cover
Loading…
| Abstract | Introduction:
The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies.
Methods:
We obtained raw microbiota data from public repositories or
via
communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses.
Results:
We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including
Streptococcus
and
Haemophilus
. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively.
Conclusion:
IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis. |
|---|---|
| AbstractList | Introduction:
The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies.
Methods:
We obtained raw microbiota data from public repositories or
via
communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses.
Results:
We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including
Streptococcus
and
Haemophilus
. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively.
Conclusion:
IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis. Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha-and beta-diversity approaches, machine learning, and biomarker analyses. Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively. Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis. Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies. Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses. Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively. Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis.Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies. Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses. Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively. Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis. Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies.Methods: We obtained raw microbiota data from public repositories or via communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses.Results: We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including Streptococcus and Haemophilus. Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively.Conclusion: IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis. The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria are associated with a given disease, or if a more general, non-specific microbiota association with disease exists, as suggested for the gut. We investigated overarching patterns of bacterial association with acute and chronic paediatric respiratory disease in an individual participant data (IPD) meta-analysis of 16S rRNA gene sequences from published respiratory microbiota studies. We obtained raw microbiota data from public repositories or communication with corresponding authors. Cross-sectional analyses of the paediatric (<18 years) microbiota in acute and chronic respiratory conditions, with >10 case subjects were included. Sequence data were processed using a uniform bioinformatics pipeline, removing a potentially substantial source of variation. Microbiota differences across diagnoses were assessed using alpha- and beta-diversity approaches, machine learning, and biomarker analyses. We ultimately included 20 studies containing individual data from 2624 children. Disease was associated with lower bacterial diversity in nasal and lower airway samples and higher relative abundances of specific nasal taxa including and . Machine learning success in assigning samples to diagnostic groupings varied with anatomical site, with positive predictive value and sensitivity ranging from 43 to 100 and 8 to 99%, respectively. IPD meta-analysis of the respiratory microbiota across multiple diseases allowed identification of a non-specific disease association which cannot be recognised by studying a single disease. Whilst imperfect, machine learning offers promise as a potential additional tool to aid clinical diagnosis. |
| Author | Marsh, Robyn L. Yi, Hana Sakwinska, Olga Everard, Mark L. Harris, J. Kirk Zheng, Yuejie Dai, Wenkui Josset, Laurence Hasegawa, Kohei Chang, Anne B. Kim, Bong-Soo Taylor, Michael W. O’Toole, George A. Ruokolainen, Lasse Cuthbertson, Leah Mansbach, Jonathan M. Pichon, Maxime Paalanen, Laura Li, Shuai C. Pillarisetti, Naveen Hoffman, Lucas R. Hong, Soo-Jong Zemanick, Edith T. Cookson, William O. C. Pettigrew, Melinda M. Seed, Patrick C. van der Gast, Christopher J. Cardenas, Paul Broderick, David T. J. Camargo, Carlos A. Mejias, Asuncion Pérez-Losada, Marcos Kelly, Matthew S. Kong, Yong Gervaix, Alain Ramilo, Octavio Wagner, Brandie D. Waite, David W. |
| AuthorAffiliation | 9 National Heart and Lung Institute, Imperial College London , London , United Kingdom 32 University of Poitiers, INSERM U1070 , Poitiers , France 22 Department of Computer Science, City University of Hong Kong, Kowloon , Hong Kong SAR , China 33 Department of Biosciences, University of Helsinki , Helsinki , Finland 38 School of Biosystem and Biomedical Science, Korea University , Seoul , South Korea 1 School of Biological Sciences, University of Auckland , Auckland , New Zealand 8 Australian Centre for Health Services Innovation, Queensland University of Technology , Brisbane, QLD , Australia 24 Department of Pediatrics, Boston Children’s Hospital , Boston, MA , United States 10 Royal Brompton and Harefield NHS Foundation Trust , London , United Kingdom 25 Division of Pediatric Infectious Diseases, Department of Pediatrics, Center for Vaccines and Immunity, Abigail Wexner Research Institute at Nationwide Children’s Hospital, The Ohio State University College of Medicine , Columbus, OH , United |
| AuthorAffiliation_xml | – name: 9 National Heart and Lung Institute, Imperial College London , London , United Kingdom – name: 24 Department of Pediatrics, Boston Children’s Hospital , Boston, MA , United States – name: 19 Division of Pediatric Infectious Diseases, Duke University , Durham, NC , United States – name: 21 Department of Biostatistics, Yale School of Public Health, Yale University , New Haven, CT , United States – name: 10 Royal Brompton and Harefield NHS Foundation Trust , London , United Kingdom – name: 33 Department of Biosciences, University of Helsinki , Helsinki , Finland – name: 35 Department of Pediatrics, Feinberg School of Medicine, Northwestern University , Chicago, IL , United States – name: 38 School of Biosystem and Biomedical Science, Korea University , Seoul , South Korea – name: 18 Hospices Civils de Lyon , Lyon , France – name: 4 Department of Epidemiology, Harvard T.H. Chan School of Public Health , Boston, MA , United States – name: 11 Department of Obstetrics and Gynecology, Peking University Shenzhen Hospital , Shenzhen , China – name: 8 Australian Centre for Health Services Innovation, Queensland University of Technology , Brisbane, QLD , Australia – name: 36 Department of Life Sciences, Manchester Metropolitan University , Manchester , United Kingdom – name: 29 CIBIO-InBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão , Vairão , Portugal – name: 27 Finnish Institute for Health and Welfare (THL) , Helsinki , Finland – name: 37 Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado, Aurora , Aurora, CO , United States – name: 28 Department of Biostatistics and Bioinformatics, Computational Biology Institute, Milken Institute School of Public Health, George Washington University , Washington, DC , United States – name: 2 Child Health Division, Menzies School of Health Research, Charles Darwin University , Darwin, NT , Australia – name: 40 Starship Children’s Hospital , Auckland , New Zealand – name: 22 Department of Computer Science, City University of Hong Kong, Kowloon , Hong Kong SAR , China – name: 1 School of Biological Sciences, University of Auckland , Auckland , New Zealand – name: 13 Department of Pediatrics, Gynecology and Obstetrics, Faculty of Medicine, University Hospitals of Geneva , Geneva , Switzerland – name: 30 Department of Epidemiology of Microbial Diseases, Yale School of Public Health , New Haven, CT , United States – name: 7 Department of Respiratory and Sleep Medicine, Queensland Children’s Hospital , Brisbane, QLD , Australia – name: 16 Department of Pediatrics and Microbiology, University of Washington , Seattle, WA , United States – name: 31 CHU Poitiers, Infectious Agents Department , Poitiers , France – name: 15 Seattle Children’s Hospital , Seattle, WA , United States – name: 32 University of Poitiers, INSERM U1070 , Poitiers , France – name: 12 School of Medicine, University of Western Australia , Perth, WA , Australia – name: 26 Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth , Hanover, NH , United States – name: 39 Shenzhen Children’s Hospital , Shenzhen , China – name: 5 Harvard Medical School , Boston, MA , United States – name: 14 Department of Pediatrics, University of Colorado School of Medicine , Aurora, CO , United States – name: 25 Division of Pediatric Infectious Diseases, Department of Pediatrics, Center for Vaccines and Immunity, Abigail Wexner Research Institute at Nationwide Children’s Hospital, The Ohio State University College of Medicine , Columbus, OH , United States – name: 3 Department of Emergency Medicine, Massachusetts General Hospital , Boston, MA , United States – name: 6 Colegio de Ciencias Biológicas y Ambientales, Instituto de Microbiología, Universidad San Francisco de Quito , Quito , Ecuador – name: 20 Department of Life Science, Multidisciplinary Genome Institute, Hallym University , Chuncheon , South Korea – name: 17 Department of Pediatrics, Childhood Asthma Atopy Center, Humidifier Disinfectant Health Center, Asan Medical Center, University of Ulsan College of Medicine , Seoul , South Korea – name: 23 Department of Biomedical Engineering, City University of Hong Kong, Kowloon , Hong Kong SAR , China – name: 34 Nestlé Research , Lausanne , Switzerland |
| Author_xml | – sequence: 1 givenname: David T. J. surname: Broderick fullname: Broderick, David T. J. – sequence: 2 givenname: David W. surname: Waite fullname: Waite, David W. – sequence: 3 givenname: Robyn L. surname: Marsh fullname: Marsh, Robyn L. – sequence: 4 givenname: Carlos A. surname: Camargo fullname: Camargo, Carlos A. – sequence: 5 givenname: Paul surname: Cardenas fullname: Cardenas, Paul – sequence: 6 givenname: Anne B. surname: Chang fullname: Chang, Anne B. – sequence: 7 givenname: William O. C. surname: Cookson fullname: Cookson, William O. C. – sequence: 8 givenname: Leah surname: Cuthbertson fullname: Cuthbertson, Leah – sequence: 9 givenname: Wenkui surname: Dai fullname: Dai, Wenkui – sequence: 10 givenname: Mark L. surname: Everard fullname: Everard, Mark L. – sequence: 11 givenname: Alain surname: Gervaix fullname: Gervaix, Alain – sequence: 12 givenname: J. Kirk surname: Harris fullname: Harris, J. Kirk – sequence: 13 givenname: Kohei surname: Hasegawa fullname: Hasegawa, Kohei – sequence: 14 givenname: Lucas R. surname: Hoffman fullname: Hoffman, Lucas R. – sequence: 15 givenname: Soo-Jong surname: Hong fullname: Hong, Soo-Jong – sequence: 16 givenname: Laurence surname: Josset fullname: Josset, Laurence – sequence: 17 givenname: Matthew S. surname: Kelly fullname: Kelly, Matthew S. – sequence: 18 givenname: Bong-Soo surname: Kim fullname: Kim, Bong-Soo – sequence: 19 givenname: Yong surname: Kong fullname: Kong, Yong – sequence: 20 givenname: Shuai C. surname: Li fullname: Li, Shuai C. – sequence: 21 givenname: Jonathan M. surname: Mansbach fullname: Mansbach, Jonathan M. – sequence: 22 givenname: Asuncion surname: Mejias fullname: Mejias, Asuncion – sequence: 23 givenname: George A. surname: O’Toole fullname: O’Toole, George A. – sequence: 24 givenname: Laura surname: Paalanen fullname: Paalanen, Laura – sequence: 25 givenname: Marcos surname: Pérez-Losada fullname: Pérez-Losada, Marcos – sequence: 26 givenname: Melinda M. surname: Pettigrew fullname: Pettigrew, Melinda M. – sequence: 27 givenname: Maxime surname: Pichon fullname: Pichon, Maxime – sequence: 28 givenname: Octavio surname: Ramilo fullname: Ramilo, Octavio – sequence: 29 givenname: Lasse surname: Ruokolainen fullname: Ruokolainen, Lasse – sequence: 30 givenname: Olga surname: Sakwinska fullname: Sakwinska, Olga – sequence: 31 givenname: Patrick C. surname: Seed fullname: Seed, Patrick C. – sequence: 32 givenname: Christopher J. surname: van der Gast fullname: van der Gast, Christopher J. – sequence: 33 givenname: Brandie D. surname: Wagner fullname: Wagner, Brandie D. – sequence: 34 givenname: Hana surname: Yi fullname: Yi, Hana – sequence: 35 givenname: Edith T. surname: Zemanick fullname: Zemanick, Edith T. – sequence: 36 givenname: Yuejie surname: Zheng fullname: Zheng, Yuejie – sequence: 37 givenname: Naveen surname: Pillarisetti fullname: Pillarisetti, Naveen – sequence: 38 givenname: Michael W. surname: Taylor fullname: Taylor, Michael W. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35002989$$D View this record in MEDLINE/PubMed https://hal.science/hal-03591509$$DView record in HAL |
| BookMark | eNp1kktv1DAUhSNUREvpD2CDsoRFBj_jmAXS0AIdaRAVD4mddePYU1cZe2p7Rpp_TzJpUVsJL2zr-p7PR_Z5WRz54E1RvMZoRmkj39u10-2MIIJnAmNM2bPiBNc1qygif44e7I-Ls5Ru0DAYIsP8ojimHCEiG3lSdJ9AZxMd9OVPt_KQt9GkMtjyCkznIEenyx8mbVyEHOK-vHDJQDIfyrkvF75zO9dtB-0VxOy024DP5QVkKL-ZDNXcQ79PLr0qnlvokzm7W0-L318-_zq_rJbfvy7O58tKMylzBVJwyxixnBLGUS1pIwhDgpNGypq2tq47I6VuBSassy2hSBvOhidgjDLM6GmxmLhdgBu1iW4Nca8COHUohLhSB5-9UZoIYglpsJCIcQJARUdYa9uWI9s03cD6OLE223ZtOm18jtA_gj4-8e5arcJONYLSmokB8G4CXD-RXc6XaqwhyiXmSO7w0Pv27rIYbrcmZbV2SZu-B2_CNilS44ZjSuoR--ahr3_k-y8dGsTUoGNIKRqrtMuQXRhtul5hpMb8qEN-1JgfNeVnUOInynv4_zV_Aeq6xzg |
| CitedBy_id | crossref_primary_10_1128_jb_00176_24 crossref_primary_10_3389_fmicb_2023_1239167 crossref_primary_10_1183_13993003_00378_2022 crossref_primary_10_1111_mec_17153 crossref_primary_10_3390_jcm13010171 crossref_primary_10_1186_s40168_023_01499_w crossref_primary_10_3389_fped_2024_1391290 |
| Cites_doi | 10.21037/jtd.2016.04.63 10.1016/j.jcf.2020.05.013 10.1016/S2213-2600(18)30171-1 10.1111/1462-2920.13647 10.1093/bioinformatics/btq461 10.1111/all.13331 10.1164/rccm.202005-1686ed 10.1164/rccm.202008-3146ed 10.1186/s40168-016-0182-1 10.1016/j.ccm.2016.11.001 10.1186/s40168-017-0385-0 10.1002/ppul.10127 10.1038/ismej.2017.119 10.7717/peerj.3362 10.1186/s40168-016-0170-5 10.3390/genes8120380 10.1371/journal.pone.0046803 10.1128/AEM.01541-09 10.1164/rccm.201104-0655OC 10.1007/978-3-319-24277-4 10.1513/AnnalsATS.201312-456OC 10.1164/rccm.202004-0959ed 10.1101/2021.05.10.443486 10.1093/nar/gks1219 10.1038/s41467-017-01973-8 10.1016/S2213-2600(18)30100-0 10.1371/journal.pcbi.1006102 10.1038/nrmicro.2017.14 10.7717/peerj.4600 10.15172/pneu.2015.6/671 10.3389/fmicb.2015.00134 10.1371/journal.pone.0190075 10.1183/13993003.00832-2017 10.1099/jgv.0.000920 10.1097/INF.0000000000001607 10.1186/s12879-014-0583-3 10.1093/bioinformatics/btr381 10.1111/cea.12895 10.1111/1462-2920.13632 10.1016/j.chom.2015.03.008 10.1186/s12859-021-04193-6 10.1164/rccm.201602-0220OC 10.1186/2049-2618-2-14 10.3389/fmicb.2014.00223 10.1183/13993003.00704-2019 10.1128/JCM.03280-13 10.1164/rccm.201407-1240oc 10.1513/AnnalsATS.201407-310OC 10.1586/ers.11.76 10.1186/gb-2011-12-6-r60 10.1186/s41479-018-0051-8 10.1371/journal.pone.0164510 10.1186/s12879-016-1670-4 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Broderick, Waite, Marsh, Camargo, Cardenas, Chang, Cookson, Cuthbertson, Dai, Everard, Gervaix, Harris, Hasegawa, Hoffman, Hong, Josset, Kelly, Kim, Kong, Li, Mansbach, Mejias, O’Toole, Paalanen, Pérez-Losada, Pettigrew, Pichon, Ramilo, Ruokolainen, Sakwinska, Seed, van der Gast, Wagner, Yi, Zemanick, Zheng, Pillarisetti and Taylor. Attribution Copyright © 2021 Broderick, Waite, Marsh, Camargo, Cardenas, Chang, Cookson, Cuthbertson, Dai, Everard, Gervaix, Harris, Hasegawa, Hoffman, Hong, Josset, Kelly, Kim, Kong, Li, Mansbach, Mejias, O’Toole, Paalanen, Pérez-Losada, Pettigrew, Pichon, Ramilo, Ruokolainen, Sakwinska, Seed, van der Gast, Wagner, Yi, Zemanick, Zheng, Pillarisetti and Taylor. 2021 Broderick, Waite, Marsh, Camargo, Cardenas, Chang, Cookson, Cuthbertson, Dai, Everard, Gervaix, Harris, Hasegawa, Hoffman, Hong, Josset, Kelly, Kim, Kong, Li, Mansbach, Mejias, O’Toole, Paalanen, Pérez-Losada, Pettigrew, Pichon, Ramilo, Ruokolainen, Sakwinska, Seed, van der Gast, Wagner, Yi, Zemanick, Zheng, Pillarisetti and Taylor |
| Copyright_xml | – notice: Copyright © 2021 Broderick, Waite, Marsh, Camargo, Cardenas, Chang, Cookson, Cuthbertson, Dai, Everard, Gervaix, Harris, Hasegawa, Hoffman, Hong, Josset, Kelly, Kim, Kong, Li, Mansbach, Mejias, O’Toole, Paalanen, Pérez-Losada, Pettigrew, Pichon, Ramilo, Ruokolainen, Sakwinska, Seed, van der Gast, Wagner, Yi, Zemanick, Zheng, Pillarisetti and Taylor. – notice: Attribution – notice: Copyright © 2021 Broderick, Waite, Marsh, Camargo, Cardenas, Chang, Cookson, Cuthbertson, Dai, Everard, Gervaix, Harris, Hasegawa, Hoffman, Hong, Josset, Kelly, Kim, Kong, Li, Mansbach, Mejias, O’Toole, Paalanen, Pérez-Losada, Pettigrew, Pichon, Ramilo, Ruokolainen, Sakwinska, Seed, van der Gast, Wagner, Yi, Zemanick, Zheng, Pillarisetti and Taylor. 2021 Broderick, Waite, Marsh, Camargo, Cardenas, Chang, Cookson, Cuthbertson, Dai, Everard, Gervaix, Harris, Hasegawa, Hoffman, Hong, Josset, Kelly, Kim, Kong, Li, Mansbach, Mejias, O’Toole, Paalanen, Pérez-Losada, Pettigrew, Pichon, Ramilo, Ruokolainen, Sakwinska, Seed, van der Gast, Wagner, Yi, Zemanick, Zheng, Pillarisetti and Taylor |
| DBID | AAYXX CITATION NPM 7X8 1XC VOOES 5PM DOA |
| DOI | 10.3389/fmicb.2021.711134 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic Hyper Article en Ligne (HAL) Hyper Article en Ligne (HAL) (Open Access) PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic CrossRef PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1664-302X |
| ExternalDocumentID | oai_doaj_org_article_c272f2281790452aa37d24bfbb50f88d PMC8733647 oai_HAL_hal_03591509v1 35002989 10_3389_fmicb_2021_711134 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NCATS NIH HHS grantid: UL1 TR001863 |
| GroupedDBID | 53G 5VS 9T4 AAFWJ AAKDD AAYXX ACGFO ACGFS ACXDI ADBBV ADRAZ AENEX AFPKN ALMA_UNASSIGNED_HOLDINGS AOIJS BAWUL BCNDV CITATION DIK ECGQY GROUPED_DOAJ GX1 HYE KQ8 M48 M~E O5R O5S OK1 PGMZT RNS RPM IPNFZ NPM RIG 7X8 1XC VOOES 5PM |
| ID | FETCH-LOGICAL-c499t-a975f442f5324506938724075289963bf66de99cb7124dfb230ce542024434143 |
| IEDL.DBID | M48 |
| ISSN | 1664-302X |
| IngestDate | Wed Aug 27 01:31:25 EDT 2025 Thu Aug 21 18:32:32 EDT 2025 Wed Jul 23 06:31:21 EDT 2025 Fri Sep 05 13:05:53 EDT 2025 Thu Apr 03 07:03:21 EDT 2025 Thu Apr 24 23:09:08 EDT 2025 Tue Jul 01 04:33:58 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | paediatrics meta-analysis microbiota (16S) individual participant data (IPD) meta-analysis respiratory infection respiratory tract |
| Language | English |
| License | Copyright © 2021 Broderick, Waite, Marsh, Camargo, Cardenas, Chang, Cookson, Cuthbertson, Dai, Everard, Gervaix, Harris, Hasegawa, Hoffman, Hong, Josset, Kelly, Kim, Kong, Li, Mansbach, Mejias, O’Toole, Paalanen, Pérez-Losada, Pettigrew, Pichon, Ramilo, Ruokolainen, Sakwinska, Seed, van der Gast, Wagner, Yi, Zemanick, Zheng, Pillarisetti and Taylor. Attribution: http://creativecommons.org/licenses/by This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c499t-a975f442f5324506938724075289963bf66de99cb7124dfb230ce542024434143 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 PMCID: PMC8733647 Edited by: Leonard Peruski, Centers for Disease Control and Prevention (CDC), United States Reviewed by: Benjamin G. Wu, New York University, United States; Celine Pattaroni, Monash University, Australia This article was submitted to Infectious Agents and Disease, a section of the journal Frontiers in Microbiology |
| ORCID | 0000-0002-3732-6894 0000-0002-0565-5239 0000-0002-5983-8006 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.3389/fmicb.2021.711134 |
| PMID | 35002989 |
| PQID | 2618513267 |
| PQPubID | 23479 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_c272f2281790452aa37d24bfbb50f88d pubmedcentral_primary_oai_pubmedcentral_nih_gov_8733647 hal_primary_oai_HAL_hal_03591509v1 proquest_miscellaneous_2618513267 pubmed_primary_35002989 crossref_citationtrail_10_3389_fmicb_2021_711134 crossref_primary_10_3389_fmicb_2021_711134 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2021-12-23 |
| PublicationDateYYYYMMDD | 2021-12-23 |
| PublicationDate_xml | – month: 12 year: 2021 text: 2021-12-23 day: 23 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland |
| PublicationTitle | Frontiers in microbiology |
| PublicationTitleAlternate | Front Microbiol |
| PublicationYear | 2021 |
| Publisher | Frontiers Media Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Media – name: Frontiers Media S.A |
| References | Grimwood (B19) 2015; 6 Zemanick (B57) 2015; 12 Wagner Mackenzie (B48) 2017; 19 Zemanick (B56) 2017; 50 Cardenas (B7) 2012; 7 Huang (B21) 2011; 5 Perez-Losada (B34) 2016; 4 Biesbroek (B3) 2014; 190 Callahan (B6) 2017; 11 Wickham (B52) 2016 Quast (B37) 2012; 41 Langevin (B25) 2017; 98 (B38) 2019 Ritchie (B39) 2020; 3 Nearing (B32) 2021 Astudillo-García (B2) 2017; 19 Marsh (B30) 2016; 4 Pillarisetti (B36) 2019; 54 Yi (B55) 2014; 14 Cuthbertson (B12) 2020; 20 Edgar (B16) 2011; 27 de Steenhuijsen Piters (B13) 2016; 194 Sakwinska (B42) 2014; 52 Wang (B51) 2016; 8 Zhang (B58) 2020; 202 Charlson (B8) 2011; 184 Segata (B44) 2011; 12 Duvallet (B14) 2017; 8 Waite (B49) 2014; 5 Cox (B10) 2019 Jari (B22) 2015 Marsh (B31) 2018; 10 Pedregosa (B33) 2011; 12 Lu (B27) 2017; 8 Biswas (B4) 2015; 6 Teo (B46) 2015; 17 Sokolova (B45) 2006 Bui (B5) 2018; 6 Li (B26) 2016; 11 Chen (B9) 2018; 6 Ruokolainen (B41) 2017; 47 Ali (B1) 2020; 12 Williamson (B53) 2017; 5 Ronchetti (B40) 2018; 6 Hampton (B20) 2014; 2 Pettigrew (B35) 2016; 16 van der Gast (B47) 2014; 11 Kelly (B23) 2017; 36 Cuthbertson (B11) 2017; 12 Wylie (B54) 2017; 38 Schloss (B43) 2009; 75 Wallen (B50) 2021; 22 Luna (B28) 2018; 6 Kim (B24) 2017; 73 Man (B29) 2017; 15 Edgar (B15) 2010; 26 Gibbons (B18) 2018; 14 Emerson (B17) 2002; 34 |
| References_xml | – volume: 8 start-page: 1316 year: 2016 ident: B51 article-title: Mycoplasma pneumoniae and Streptococcus pneumoniae caused different microbial structure and correlation network in lung microbiota. publication-title: J. Thorac. Dis. doi: 10.21037/jtd.2016.04.63 – volume: 20 start-page: 295 year: 2020 ident: B12 article-title: The fungal airway microbiome in cystic fibrosis and non-cystic fibrosis bronchiectasis. publication-title: J. Cystic Fibros doi: 10.1016/j.jcf.2020.05.013 – volume: 6 start-page: 461 year: 2018 ident: B40 article-title: The CF-Sputum Induction Trial (CF-SpIT) to assess lower airway bacterial sampling in young children with cystic fibrosis: a prospective internally controlled interventional trial. publication-title: Lancet Respir. Med. doi: 10.1016/S2213-2600(18)30171-1 – volume: 19 start-page: 1450 year: 2017 ident: B2 article-title: Evaluating the core microbiota in complex communities: a systematic investigation. publication-title: Environ. Microbiol. doi: 10.1111/1462-2920.13647 – volume: 26 start-page: 2460 year: 2010 ident: B15 article-title: Search and clustering orders of magnitude faster than BLAST publication-title: Bioinformtaics doi: 10.1093/bioinformatics/btq461 – volume: 73 start-page: 644 year: 2017 ident: B24 article-title: Different functional genes of upper airway microbiome associated with natural course of childhood asthma. publication-title: Allergy doi: 10.1111/all.13331 – volume: 3 start-page: 321 year: 2020 ident: B39 article-title: Metagenomic characterization of the respiratory microbiome: a pièce de résistance. publication-title: Am. J. Respir. Crit. Care Med. doi: 10.1164/rccm.202005-1686ed – volume: 12 start-page: 1616 year: 2020 ident: B1 article-title: In pursuit of microbiome-based therapies for acute respiratory failure. publication-title: Am. J. Respir. Crit. Care Med. doi: 10.1164/rccm.202008-3146ed – volume: 4 year: 2016 ident: B30 article-title: The microbiota in bronchoalveolar lavage from young children with chronic lung disease includes taxa present in both the oropharynx and nasopharynx. publication-title: Microbiome doi: 10.1186/s40168-016-0182-1 – volume: 38 start-page: 11 year: 2017 ident: B54 article-title: The virome of the human respiratory tract. publication-title: Clin. Chest Med. doi: 10.1016/j.ccm.2016.11.001 – volume: 6 year: 2018 ident: B28 article-title: The association between anterior nares and nasopharyngeal microbiota in infants hospitalized for bronchiolitis. publication-title: Microbiome doi: 10.1186/s40168-017-0385-0 – volume: 34 start-page: 91 year: 2002 ident: B17 article-title: Pseudomonas aeruginosa and other predictors of mortality and morbidity in young children with cystic fibrosis. publication-title: Pediatr. Pulmonol. doi: 10.1002/ppul.10127 – volume: 11 year: 2017 ident: B6 article-title: Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. publication-title: ISME J. doi: 10.1038/ismej.2017.119 – year: 2019 ident: B10 article-title: The lung microbiome publication-title: European Respiratory Society – volume: 5 year: 2017 ident: B53 article-title: Impact of enzymatic digestion on bacterial community composition in CF airway samples. publication-title: PeerJ doi: 10.7717/peerj.3362 – volume: 4 year: 2016 ident: B34 article-title: Two sampling methods yield distinct microbial signatures in the nasopharynges of asthmatic children. publication-title: Microbiome doi: 10.1186/s40168-016-0170-5 – volume: 8 year: 2017 ident: B27 article-title: The alteration of nasopharyngeal and oropharyngeal microbiota in children with MPP and non-MPP. publication-title: Genes doi: 10.3390/genes8120380 – volume: 7 year: 2012 ident: B7 article-title: Upper airways microbiota in antibiotic-naive wheezing and healthy infants from the tropics of rural Ecuador. publication-title: PLoS One doi: 10.1371/journal.pone.0046803 – volume: 75 start-page: 7537 year: 2009 ident: B43 article-title: Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities publication-title: Appl. Environ. Microbiol doi: 10.1128/AEM.01541-09 – volume: 184 start-page: 957 year: 2011 ident: B8 article-title: Topographical continuity of bacterial populations in the healthy human respiratory tract. publication-title: Am. J. Respir. Crit. Care Med. doi: 10.1164/rccm.201104-0655OC – year: 2016 ident: B52 publication-title: ggplot2: Elegant Graphics For Data Analysis. doi: 10.1007/978-3-319-24277-4 – volume: 11 start-page: 1039 year: 2014 ident: B47 article-title: Three clinically distinct chronic pediatric airway infections share a common core microbiota. publication-title: Ann. Am. Thorac. Soc. doi: 10.1513/AnnalsATS.201312-456OC – volume: 202 start-page: 159 year: 2020 ident: B58 article-title: The origins of COPD: sometimes the journey matters more than the destination. publication-title: Am. J. Respir. Crit. Care Med. doi: 10.1164/rccm.202004-0959ed – year: 2021 ident: B32 article-title: Microbiome differential abundance methods produce disturbingly different results across 38 datasets. publication-title: bioRxiv doi: 10.1101/2021.05.10.443486 – volume: 41 start-page: D590 year: 2012 ident: B37 article-title: The SILVA ribosomal RNA gene database project: improved data processing and web-based tools publication-title: Nucleic Acids Res doi: 10.1093/nar/gks1219 – volume: 12 start-page: 2825 year: 2011 ident: B33 article-title: Scikit-learn: machine learning in Python. publication-title: J. Mach. Learn. Res. – volume: 8 year: 2017 ident: B14 article-title: Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. publication-title: Nat. Commun. doi: 10.1038/s41467-017-01973-8 – volume: 6 start-page: 535 year: 2018 ident: B5 article-title: Childhood predictors of lung function trajectories and future COPD risk: a prospective cohort study from the first to the sixth decade of life. publication-title: Lancet Respir. Med. doi: 10.1016/S2213-2600(18)30100-0 – year: 2015 ident: B22 publication-title: Vegan: Community Ecology Package. – volume: 14 year: 2018 ident: B18 article-title: Correcting for batch effects in case-control microbiome studies. publication-title: PLoS Comput. Biol. doi: 10.1371/journal.pcbi.1006102 – volume: 15 year: 2017 ident: B29 article-title: The microbiota of the respiratory tract: gatekeeper to respiratory health. publication-title: Nat. Rev. Microbiol. doi: 10.1038/nrmicro.2017.14 – volume: 6 year: 2018 ident: B9 article-title: A robust normalization method for zero-inflated count data with application to microbiome sequencing data. publication-title: PeerJ doi: 10.7717/peerj.4600 – volume: 6 start-page: 101 year: 2015 ident: B19 article-title: Long-term effects of pneumonia in young children. publication-title: Pneumonia doi: 10.15172/pneu.2015.6/671 – volume: 6 year: 2015 ident: B4 article-title: The nasal microbiota in health and disease: variation within and between subjects. publication-title: Front. Microbiol. doi: 10.3389/fmicb.2015.00134 – volume: 12 year: 2017 ident: B11 article-title: The impact of persistent bacterial bronchitis on the pulmonary microbiome of children. publication-title: PLoS One doi: 10.1371/journal.pone.0190075 – volume: 50 year: 2017 ident: B56 article-title: Airway microbiota across age and disease spectrum in cystic fibrosis. publication-title: Eur. Respir. J. doi: 10.1183/13993003.00832-2017 – year: 2006 ident: B45 article-title: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation publication-title: Proceedings of the Australasian Joint Conference On Artificial Intelligence. – volume: 98 start-page: 2425 year: 2017 ident: B25 article-title: Early nasopharyngeal microbial signature associated with severe influenza in children: a retrospective pilot study. publication-title: J. Gen. Virol. doi: 10.1099/jgv.0.000920 – volume: 36 start-page: e211 year: 2017 ident: B23 article-title: The nasopharyngeal microbiota of children with respiratory infections in Botswana. publication-title: Pediatr. Infect. Dis. J. doi: 10.1097/INF.0000000000001607 – volume: 14 year: 2014 ident: B55 article-title: Profiling bacterial community in upper respiratory tracts. publication-title: BMC Infect. Dis. doi: 10.1186/s12879-014-0583-3 – volume: 27 start-page: 2194 year: 2011 ident: B16 article-title: UCHIME improves sensitivity and speed of chimera detection publication-title: Bioinformatics doi: 10.1093/bioinformatics/btr381 – volume: 47 start-page: 665 year: 2017 ident: B41 article-title: Significant disparities in allergy prevalence and microbiota between the young people in Finnish and Russian Karelia. publication-title: Clin. Exp. Allergy doi: 10.1111/cea.12895 – volume: 19 start-page: 381 year: 2017 ident: B48 article-title: Bacterial community collapse: a meta-analysis of the sinonasal microbiota in chronic rhinosinusitis. publication-title: Environ. Microbiol. doi: 10.1111/1462-2920.13632 – volume: 17 start-page: 704 year: 2015 ident: B46 article-title: The infant nasopharyngeal microbiome impacts severity of lower respiratory infection and risk of asthma development. publication-title: Cell Host Microbe doi: 10.1016/j.chom.2015.03.008 – volume: 22 year: 2021 ident: B50 article-title: Comparison study of differential abundance testing methods using two large Parkinson disease gut microbiome datasets derived from 16S amplicon sequencing. publication-title: BMC Bioinform. doi: 10.1186/s12859-021-04193-6 – volume: 194 start-page: 1104 year: 2016 ident: B13 article-title: Nasopharyngeal microbiota, host transcriptome, and disease severity in children with respiratory syncytial virus infection. publication-title: Am. J. Respir. Crit. Care Med. doi: 10.1164/rccm.201602-0220OC – year: 2019 ident: B38 publication-title: R: A Language and Environment for Statistical Computing – volume: 2 year: 2014 ident: B20 article-title: The microbiome in pediatric cystic fibrosis patients: the role of shared environment suggests a window of intervention. publication-title: Microbiome doi: 10.1186/2049-2618-2-14 – volume: 5 year: 2014 ident: B49 article-title: Characterizing the avian gut microbiota: membership, driving influences, and potential function. publication-title: Front. Microbiol. doi: 10.3389/fmicb.2014.00223 – volume: 54 year: 2019 ident: B36 article-title: The airway microbiota in children newly diagnosed with bronchiectasis largely retains its diversity. publication-title: Eur. Respir. J doi: 10.1183/13993003.00704-2019 – volume: 52 start-page: 1590 year: 2014 ident: B42 article-title: Nasopharyngeal microbiota in healthy children and pneumonia patients. publication-title: J. Clin. Microbiol. doi: 10.1128/JCM.03280-13 – volume: 190 start-page: 1283 year: 2014 ident: B3 article-title: Early respiratory microbiota composition determines bacterial succession patterns and respiratory health in children. publication-title: Am. J. Respir. Crit. Care Med. doi: 10.1164/rccm.201407-1240oc – volume: 12 start-page: 221 year: 2015 ident: B57 article-title: Assessment of airway microbiota and inflammation in cystic fibrosis using multiple sampling methods. publication-title: Ann. Am. Thorac. Soc. doi: 10.1513/AnnalsATS.201407-310OC – volume: 5 start-page: 809 year: 2011 ident: B21 article-title: The emerging relationship between the airway microbiota and chronic respiratory disease: clinical implications. publication-title: Expert. Rev. Respir. Med. doi: 10.1586/ers.11.76 – volume: 12 year: 2011 ident: B44 article-title: Metagenomic biomarker discovery and explanation. publication-title: Genome Biol. doi: 10.1186/gb-2011-12-6-r60 – volume: 10 year: 2018 ident: B31 article-title: How low can we go? The implications of low bacterial load in respiratory microbiota studies. publication-title: Pneumonia doi: 10.1186/s41479-018-0051-8 – volume: 11 year: 2016 ident: B26 article-title: Data mining of lung microbiota in cystic fibrosis patients. publication-title: PLoS One doi: 10.1371/journal.pone.0164510 – volume: 16 year: 2016 ident: B35 article-title: Association of sputum microbiota profiles with severity of community-acquired pneumonia in children. publication-title: BMC Infect. Dis. doi: 10.1186/s12879-016-1670-4 |
| SSID | ssj0000402000 |
| Score | 2.3489745 |
| Snippet | Introduction:
The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether... The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether particular bacteria... Introduction: The airway microbiota has been linked to specific paediatric respiratory diseases, but studies are often small. It remains unclear whether... |
| SourceID | doaj pubmedcentral hal proquest pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 711134 |
| SubjectTerms | Bacteriology Human health and pathology individual participant data (IPD) meta-analysis Infectious diseases Life Sciences meta-analysis Microbiology Microbiology and Parasitology microbiota (16S) paediatrics Quantitative Methods respiratory infection respiratory tract Santé publique et épidémiologie Virology |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1La9wwEB5KINBLaZI-nBdK6ang1pEly85t82JbmlLaBnITkiUlC8EbEieQf58Zy7tZt5BecrVkW9aMNN-Hxt8AfLQZr11mQ0pqlqkQmU0NhnVcVy4XFoceHP2cfPKjGJ-Kb2fybKHUF-WERXngOHFfaq544LwkJSkhuTG5clzYYK3MQlk62n0x5i2QqW4PJlqU9ceYyMIqNNOktsgH-e5nReXVxSAQdXr9GF4uKBvyX6j5d8bkQgg6fg2veuzIRnHMK_DCN6uwHKtJ3q-B24_Ky9jl9-Q8KnbesGlgP82sHgf79Xiyzg7j2cweGzXs6_y_LOzdZ1o3LTs0rWEnvjXpTLzkDZweH_05GKd9EYW0RjLTpqZSMgjBg0ToJLOiyktFLE4S0ypyG4rC-aqqrcJI74JFSlJ7KXCihMAIJ_K3sNRMG_8emC2xHRGSr70SrjC2tEIaJ5WtAqIol0A2m1Fd9wrjVOjiUiPTICPozgiajKCjERL4NL_lKsprPNV5n8w070jK2N0F9Bfd-4v-n78k8AGNPHjGePRd0zVSM0R8XN3tJrAz8wGNy43OUEzjp7c3GgknYlTEvCqBd9En5s_KZRS0T0ANvGXwsmFLM7noJL1LUqUUav05vnADXtKkUc4Nzzdhqb2-9VuInFq73S2SB7bEFR4 priority: 102 providerName: Directory of Open Access Journals |
| Title | Bacterial Signatures of Paediatric Respiratory Disease: An Individual Participant Data Meta-Analysis |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/35002989 https://www.proquest.com/docview/2618513267 https://hal.science/hal-03591509 https://pubmed.ncbi.nlm.nih.gov/PMC8733647 https://doaj.org/article/c272f2281790452aa37d24bfbb50f88d |
| Volume | 12 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3fb9MwED6NIdBeJn6TAZNBPCFlSh07TpAQ6hijIIoQUKlvlh3HW6UphTZD7L_nLk4KgQnx0ofEcS3fXe77Yvs7gKc24aVLrI9JzTIWIrGxwbSOceVSYXHo3tHh5OmHbDIT7-ZyvgV9eatuAteXUjuqJzVbnR38-HbxEgP-BTFOzLdogUVpkerx0YGiyuniClzFxJSRk087tN--mIkrtYdSRllG6wF8HtY5L-9lB66nMkiUD5JWq-2PqeiUdk7-DUv_3F35W7o6vgG7Hc5k4-AYN2Grqm_BtVB58uI2uMOg0oxNPi9Ogrrnmi09-2j62h3s069VeHYU1nGes3HN3m7OcGHrbld23bAj0xg2rRoT90Ind2B2_PrLq0ncFVyISyQ-TWwKJb0Q3EuEWTLJijRXxPgksbIstT7LXFUUpVWICpy3SF_KSgqcMyEwG4r0LmzXy7q6D8zmeB_RVFVWSrjM2NwKaZxUtvCIuFwEST-juuzUyKkoxplGVkL20K09NNlDB3tE8GzzyNcgxfGvxodkpk1DUtFuLyxXJ7oLSl1yxT3nOamUCcmNSZXjwnprZeLzHAf5BI086GMyfq_pGikfIpYuvo8ieNz7gMbQpPUWU1fL87VGcop4FvGxiuBe8IlNX71nRaAG3jL4s-GdenHayn_npGAp1N7_DO4B7NCk0P4bnj6E7WZ1Xj1CFNXY_fbrA_6-mY_22zj5CcvKGDQ |
| linkProvider | Scholars Portal |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Bacterial+Signatures+of+Paediatric+Respiratory+Disease%3A+An+Individual+Participant+Data+Meta-Analysis&rft.jtitle=Frontiers+in+microbiology&rft.au=Broderick%2C+David&rft.au=Waite%2C+David&rft.au=Marsh%2C+Robyn&rft.au=Camargo%2C+Carlos&rft.date=2021-12-23&rft.pub=Frontiers+Media&rft.issn=1664-302X&rft.eissn=1664-302X&rft.volume=12&rft_id=info:doi/10.3389%2Ffmicb.2021.711134&rft_id=info%3Apmid%2F35002989&rft.externalDBID=HAS_PDF_LINK&rft.externalDocID=oai_HAL_hal_03591509v1 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1664-302X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1664-302X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1664-302X&client=summon |