How low can we go? The implications of low bacterial load in respiratory microbiota studies

Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial loa...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Marsh, Robyn L. [verfasserIn] Nelson, Maria T. Pope, Chris E. Leach, Amanda J. Hoffman, Lucas R. Chang, Anne B. Smith-Vaughan, Heidi C.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Microbiota 16S rRNA gene sequencing Bacterial load Low bacterial load Background contamination

Anmerkung:	© The Author(s) 2018

Übergeordnetes Werk:	Enthalten in: Pneumonia - [London] : BioMed Central, 2012, 10(2018), 1 vom: 05. Juli
Übergeordnetes Werk:	volume:10 ; year:2018 ; number:1 ; day:05 ; month:07

Links:	Volltext

DOI / URN:	10.1186/s41479-018-0051-8

Katalog-ID:	SPR038231751

Internformat


LEADER	01000caa a22002652 4500
001	SPR038231751
003	DE-627
005	20230519225512.0
007	cr uuu---uuuuu
008	201007s2018 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1186/s41479-018-0051-8 \|2 doi
035			\|a (DE-627)SPR038231751
035			\|a (SPR)s41479-018-0051-8-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	1		\|a Marsh, Robyn L. \|e verfasserin \|4 aut
245	1	0	\|a How low can we go? The implications of low bacterial load in respiratory microbiota studies
264		1	\|c 2018
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
500			\|a © The Author(s) 2018
520			\|a Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Main body Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Conclusion Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens.
650		4	\|a Microbiota \|7 (dpeaa)DE-He213
650		4	\|a 16S rRNA gene sequencing \|7 (dpeaa)DE-He213
650		4	\|a Bacterial load \|7 (dpeaa)DE-He213
650		4	\|a Low bacterial load \|7 (dpeaa)DE-He213
650		4	\|a Background contamination \|7 (dpeaa)DE-He213
700	1		\|a Nelson, Maria T. \|4 aut
700	1		\|a Pope, Chris E. \|4 aut
700	1		\|a Leach, Amanda J. \|4 aut
700	1		\|a Hoffman, Lucas R. \|4 aut
700	1		\|a Chang, Anne B. \|4 aut
700	1		\|a Smith-Vaughan, Heidi C. \|4 aut
773	0	8	\|i Enthalten in \|t Pneumonia \|d [London] : BioMed Central, 2012 \|g 10(2018), 1 vom: 05. Juli \|w (DE-627)812500563 \|w (DE-600)2803296-2 \|x 2200-6133 \|7 nnns
773	1	8	\|g volume:10 \|g year:2018 \|g number:1 \|g day:05 \|g month:07
856	4	0	\|u https://dx.doi.org/10.1186/s41479-018-0051-8 \|z kostenfrei \|3 Volltext
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_SPRINGER
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 10 \|j 2018 \|e 1 \|b 05 \|c 07

Indexfelder

author_variant	r l m rl rlm m t n mt mtn c e p ce cep a j l aj ajl l r h lr lrh a b c ab abc h c s v hcs hcsv
matchkey_str	article:22006133:2018----::olwawgtemlctosfobceilodneprt
hierarchy_sort_str	2018
publishDate	2018
allfields	10.1186/s41479-018-0051-8 doi (DE-627)SPR038231751 (SPR)s41479-018-0051-8-e DE-627 ger DE-627 rakwb eng Marsh, Robyn L. verfasserin aut How low can we go? The implications of low bacterial load in respiratory microbiota studies 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Main body Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Conclusion Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens. Microbiota (dpeaa)DE-He213 16S rRNA gene sequencing (dpeaa)DE-He213 Bacterial load (dpeaa)DE-He213 Low bacterial load (dpeaa)DE-He213 Background contamination (dpeaa)DE-He213 Nelson, Maria T. aut Pope, Chris E. aut Leach, Amanda J. aut Hoffman, Lucas R. aut Chang, Anne B. aut Smith-Vaughan, Heidi C. aut Enthalten in Pneumonia [London] : BioMed Central, 2012 10(2018), 1 vom: 05. Juli (DE-627)812500563 (DE-600)2803296-2 2200-6133 nnns volume:10 year:2018 number:1 day:05 month:07 https://dx.doi.org/10.1186/s41479-018-0051-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 1 05 07
spelling	10.1186/s41479-018-0051-8 doi (DE-627)SPR038231751 (SPR)s41479-018-0051-8-e DE-627 ger DE-627 rakwb eng Marsh, Robyn L. verfasserin aut How low can we go? The implications of low bacterial load in respiratory microbiota studies 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Main body Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Conclusion Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens. Microbiota (dpeaa)DE-He213 16S rRNA gene sequencing (dpeaa)DE-He213 Bacterial load (dpeaa)DE-He213 Low bacterial load (dpeaa)DE-He213 Background contamination (dpeaa)DE-He213 Nelson, Maria T. aut Pope, Chris E. aut Leach, Amanda J. aut Hoffman, Lucas R. aut Chang, Anne B. aut Smith-Vaughan, Heidi C. aut Enthalten in Pneumonia [London] : BioMed Central, 2012 10(2018), 1 vom: 05. Juli (DE-627)812500563 (DE-600)2803296-2 2200-6133 nnns volume:10 year:2018 number:1 day:05 month:07 https://dx.doi.org/10.1186/s41479-018-0051-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 1 05 07
allfields_unstemmed	10.1186/s41479-018-0051-8 doi (DE-627)SPR038231751 (SPR)s41479-018-0051-8-e DE-627 ger DE-627 rakwb eng Marsh, Robyn L. verfasserin aut How low can we go? The implications of low bacterial load in respiratory microbiota studies 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Main body Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Conclusion Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens. Microbiota (dpeaa)DE-He213 16S rRNA gene sequencing (dpeaa)DE-He213 Bacterial load (dpeaa)DE-He213 Low bacterial load (dpeaa)DE-He213 Background contamination (dpeaa)DE-He213 Nelson, Maria T. aut Pope, Chris E. aut Leach, Amanda J. aut Hoffman, Lucas R. aut Chang, Anne B. aut Smith-Vaughan, Heidi C. aut Enthalten in Pneumonia [London] : BioMed Central, 2012 10(2018), 1 vom: 05. Juli (DE-627)812500563 (DE-600)2803296-2 2200-6133 nnns volume:10 year:2018 number:1 day:05 month:07 https://dx.doi.org/10.1186/s41479-018-0051-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 1 05 07
allfieldsGer	10.1186/s41479-018-0051-8 doi (DE-627)SPR038231751 (SPR)s41479-018-0051-8-e DE-627 ger DE-627 rakwb eng Marsh, Robyn L. verfasserin aut How low can we go? The implications of low bacterial load in respiratory microbiota studies 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Main body Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Conclusion Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens. Microbiota (dpeaa)DE-He213 16S rRNA gene sequencing (dpeaa)DE-He213 Bacterial load (dpeaa)DE-He213 Low bacterial load (dpeaa)DE-He213 Background contamination (dpeaa)DE-He213 Nelson, Maria T. aut Pope, Chris E. aut Leach, Amanda J. aut Hoffman, Lucas R. aut Chang, Anne B. aut Smith-Vaughan, Heidi C. aut Enthalten in Pneumonia [London] : BioMed Central, 2012 10(2018), 1 vom: 05. Juli (DE-627)812500563 (DE-600)2803296-2 2200-6133 nnns volume:10 year:2018 number:1 day:05 month:07 https://dx.doi.org/10.1186/s41479-018-0051-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 1 05 07
allfieldsSound	10.1186/s41479-018-0051-8 doi (DE-627)SPR038231751 (SPR)s41479-018-0051-8-e DE-627 ger DE-627 rakwb eng Marsh, Robyn L. verfasserin aut How low can we go? The implications of low bacterial load in respiratory microbiota studies 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2018 Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Main body Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Conclusion Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens. Microbiota (dpeaa)DE-He213 16S rRNA gene sequencing (dpeaa)DE-He213 Bacterial load (dpeaa)DE-He213 Low bacterial load (dpeaa)DE-He213 Background contamination (dpeaa)DE-He213 Nelson, Maria T. aut Pope, Chris E. aut Leach, Amanda J. aut Hoffman, Lucas R. aut Chang, Anne B. aut Smith-Vaughan, Heidi C. aut Enthalten in Pneumonia [London] : BioMed Central, 2012 10(2018), 1 vom: 05. Juli (DE-627)812500563 (DE-600)2803296-2 2200-6133 nnns volume:10 year:2018 number:1 day:05 month:07 https://dx.doi.org/10.1186/s41479-018-0051-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2018 1 05 07
language	English
source	Enthalten in Pneumonia 10(2018), 1 vom: 05. Juli volume:10 year:2018 number:1 day:05 month:07
sourceStr	Enthalten in Pneumonia 10(2018), 1 vom: 05. Juli volume:10 year:2018 number:1 day:05 month:07
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Microbiota 16S rRNA gene sequencing Bacterial load Low bacterial load Background contamination
isfreeaccess_bool	true
container_title	Pneumonia
authorswithroles_txt_mv	Marsh, Robyn L. @@aut@@ Nelson, Maria T. @@aut@@ Pope, Chris E. @@aut@@ Leach, Amanda J. @@aut@@ Hoffman, Lucas R. @@aut@@ Chang, Anne B. @@aut@@ Smith-Vaughan, Heidi C. @@aut@@
publishDateDaySort_date	2018-07-05T00:00:00Z
hierarchy_top_id	812500563
id	SPR038231751
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR038231751</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519225512.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s41479-018-0051-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR038231751</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s41479-018-0051-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Marsh, Robyn L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">How low can we go? The implications of low bacterial load in respiratory microbiota studies</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Main body Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Conclusion Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Microbiota</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">16S rRNA gene sequencing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bacterial load</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Low bacterial load</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Background contamination</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nelson, Maria T.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pope, Chris E.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Leach, Amanda J.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hoffman, Lucas R.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chang, Anne B.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Smith-Vaughan, Heidi C.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Pneumonia</subfield><subfield code="d">[London] : BioMed Central, 2012</subfield><subfield code="g">10(2018), 1 vom: 05. Juli</subfield><subfield code="w">(DE-627)812500563</subfield><subfield code="w">(DE-600)2803296-2</subfield><subfield code="x">2200-6133</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:1</subfield><subfield code="g">day:05</subfield><subfield code="g">month:07</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s41479-018-0051-8</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2018</subfield><subfield code="e">1</subfield><subfield code="b">05</subfield><subfield code="c">07</subfield></datafield></record></collection>
author	Marsh, Robyn L.
spellingShingle	Marsh, Robyn L. misc Microbiota misc 16S rRNA gene sequencing misc Bacterial load misc Low bacterial load misc Background contamination How low can we go? The implications of low bacterial load in respiratory microbiota studies
authorStr	Marsh, Robyn L.
ppnlink_with_tag_str_mv	@@773@@(DE-627)812500563
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	2200-6133
topic_title	How low can we go? The implications of low bacterial load in respiratory microbiota studies Microbiota (dpeaa)DE-He213 16S rRNA gene sequencing (dpeaa)DE-He213 Bacterial load (dpeaa)DE-He213 Low bacterial load (dpeaa)DE-He213 Background contamination (dpeaa)DE-He213
topic	misc Microbiota misc 16S rRNA gene sequencing misc Bacterial load misc Low bacterial load misc Background contamination
topic_unstemmed	misc Microbiota misc 16S rRNA gene sequencing misc Bacterial load misc Low bacterial load misc Background contamination
topic_browse	misc Microbiota misc 16S rRNA gene sequencing misc Bacterial load misc Low bacterial load misc Background contamination
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Pneumonia
hierarchy_parent_id	812500563
hierarchy_top_title	Pneumonia
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)812500563 (DE-600)2803296-2
title	How low can we go? The implications of low bacterial load in respiratory microbiota studies
ctrlnum	(DE-627)SPR038231751 (SPR)s41479-018-0051-8-e
title_full	How low can we go? The implications of low bacterial load in respiratory microbiota studies
author_sort	Marsh, Robyn L.
journal	Pneumonia
journalStr	Pneumonia
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2018
contenttype_str_mv	txt
author_browse	Marsh, Robyn L. Nelson, Maria T. Pope, Chris E. Leach, Amanda J. Hoffman, Lucas R. Chang, Anne B. Smith-Vaughan, Heidi C.
container_volume	10
format_se	Elektronische Aufsätze
author-letter	Marsh, Robyn L.
doi_str_mv	10.1186/s41479-018-0051-8
title_sort	how low can we go? the implications of low bacterial load in respiratory microbiota studies
title_auth	How low can we go? The implications of low bacterial load in respiratory microbiota studies
abstract	Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Main body Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Conclusion Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens. © The Author(s) 2018
abstractGer	Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Main body Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Conclusion Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens. © The Author(s) 2018
abstract_unstemmed	Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Main body Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Conclusion Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens. © The Author(s) 2018
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	How low can we go? The implications of low bacterial load in respiratory microbiota studies
url	https://dx.doi.org/10.1186/s41479-018-0051-8
remote_bool	true
author2	Nelson, Maria T. Pope, Chris E. Leach, Amanda J. Hoffman, Lucas R. Chang, Anne B. Smith-Vaughan, Heidi C.
author2Str	Nelson, Maria T. Pope, Chris E. Leach, Amanda J. Hoffman, Lucas R. Chang, Anne B. Smith-Vaughan, Heidi C.
ppnlink	812500563
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1186/s41479-018-0051-8
up_date	2024-07-03T16:54:57.317Z
_version_	1803577665590919168
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR038231751</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519225512.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2018 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s41479-018-0051-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR038231751</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s41479-018-0051-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Marsh, Robyn L.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">How low can we go? The implications of low bacterial load in respiratory microbiota studies</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2018</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Culture-independent sequencing methods are increasingly used to investigate the microbiota associated with human mucosal surfaces, including sites that have low bacterial load in healthy individuals (e.g. the lungs). Standard microbiota methods developed for analysis of high bacterial load specimens (e.g. stool) may require modification when bacterial load is low, as background contamination derived from sterile laboratory reagents and kits can dominate sequence data when few bacteria are present. Main body Bacterial load in respiratory specimens may vary depending on the specimen type, specimen volume, the anatomic site sampled and clinical parameters. This review discusses methodological issues inherent to analysis of low bacterial load specimens and recommends strategies for successful respiratory microbiota studies. The range of methods currently used to process DNA from low bacterial load specimens, and the strategies used to identify and exclude background contamination are also discussed. Conclusion Microbiota studies that include low bacterial load specimens require additional tests to ensure that background contamination does not bias the results or interpretation. Several methods are currently used to analyse the microbiota in low bacterial load respiratory specimens; however, there is scant literature comparing the effectiveness and biases of different methods. Further research is needed to define optimal methods for analysing the microbiota in low bacterial load specimens.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Microbiota</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">16S rRNA gene sequencing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bacterial load</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Low bacterial load</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Background contamination</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nelson, Maria T.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Pope, Chris E.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Leach, Amanda J.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hoffman, Lucas R.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chang, Anne B.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Smith-Vaughan, Heidi C.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Pneumonia</subfield><subfield code="d">[London] : BioMed Central, 2012</subfield><subfield code="g">10(2018), 1 vom: 05. Juli</subfield><subfield code="w">(DE-627)812500563</subfield><subfield code="w">(DE-600)2803296-2</subfield><subfield code="x">2200-6133</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:10</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:1</subfield><subfield code="g">day:05</subfield><subfield code="g">month:07</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s41479-018-0051-8</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">10</subfield><subfield code="j">2018</subfield><subfield code="e">1</subfield><subfield code="b">05</subfield><subfield code="c">07</subfield></datafield></record></collection>
score	7.4021015

Nicht das Richtige dabei?

Schreiben Sie uns!

How low can we go? The implications of low bacterial load in respiratory microbiota studies

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?