Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Computational detection and quantification of human and mouse neutrophil extracellular traps in flow cytometry and confocal microscopy
Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi, Immunologi/immunkemi. Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR). (Constantin Urban)
Vise andre og tillknytning
2017 (engelsk)Inngår i: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, nr 1, artikkel-id 17755Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Neutrophil extracellular traps (NETs) are extracellular defense mechanisms used by neutrophils, where chromatin is expelled together with histones and granular/cytoplasmic proteins. They have become an immunology hotspot, implicated in infections, but also in a diverse array of diseases such as systemic lupus erythematosus, diabetes, and cancer. However, the precise assessment of in vivo relevance in different disease settings has been hampered by limited tools to quantify occurrence of extracellular traps in experimental models and human samples. To expedite progress towards improved quantitative tools, we have developed computational pipelines to identify extracellular traps from an in vitro human samples visualized using the ImageStream® platform (Millipore Sigma, Darmstadt, Germany), and confocal images of an in vivo mouse disease model of aspergillus fumigatus pneumonia. Our two in vitro methods, tested on n = 363/n =145 images respectively, achieved holdout sensitivity/specificity 0.98/0.93 and 1/0.92. Our unsupervised method for thin lung tissue sections in murine fungal pneumonia achieved sensitivity/specificity 0.99/0.98 in n = 14 images. Our supervised method for thin lung tissue classified NETs with sensitivity/specificity 0.86/0.90. We expect that our approach will be of value for researchers, and have application in infectious and inflammatory diseases.

sted, utgiver, år, opplag, sider
Nature Publishing Group, 2017. Vol. 7, nr 1, artikkel-id 17755
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-143398DOI: 10.1038/s41598-017-18099-yISI: 000418359600005PubMedID: 29259241OAI: oai:DiVA.org:umu-143398DiVA, id: diva2:1168792
Tilgjengelig fra: 2017-12-21 Laget: 2017-12-21 Sist oppdatert: 2018-06-09bibliografisk kontrollert

Open Access i DiVA

fulltext(11604 kB)101 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 11604 kBChecksum SHA-512
3e4cdb6a0289b3cd382d6d4c8b9e4788008c79b51ad55dd3dff6d3b1fea28822d5aa055db4f34f97df18018461a35986666341290fde532b8c022c0120007525
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekstPubMed

Søk i DiVA

Av forfatter/redaktør
Urban, Constantin F
Av organisasjonen
I samme tidsskrift
Scientific Reports

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 101 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
pubmed
urn-nbn

Altmetric

doi
pubmed
urn-nbn
Totalt: 457 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf