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1.
  • Cossarizza, A., et al. (författare)
  • Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)
  • 2019
  • Ingår i: European Journal of Immunology. - : Wiley. - 0014-2980 .- 1521-4141. ; 49:10, s. 1457-1973
  • Tidskriftsartikel (refereegranskat)abstract
    • These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.
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2.
  • Demichev, Vadim, et al. (författare)
  • A time-resolved proteomic and prognostic map of COVID-19
  • 2021
  • Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 12:8, s. 780-794.e7
  • Tidskriftsartikel (refereegranskat)abstract
    • COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.
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