% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Reichmann:607553,
      author       = {Reichmann, Jakob and Sarrazin, Clement and Schmale,
                      Sebastian and Blaurock, Claudia and Balkema-Buschmann, Anne
                      and Schmitzer, Bernhard and Salditt, Tim},
      title        = {3{D} imaging of {SARS}-{C}o{V}-2 infected hamster lungs by
                      {X}-ray phase contrast tomography enables drug testing},
      journal      = {Scientific reports},
      volume       = {14},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {PUBDB-2024-01925},
      pages        = {12348},
      year         = {2024},
      abstract     = {X-ray Phase Contrast Tomography (XPCT) based on wavefield
                      propagation has been established as a high resolution
                      three-dimensional (3D) imaging modality, suitable to
                      reconstruct the intricate structure of soft tissues, and the
                      corresponding pathological alterations. However, for
                      biomedical research, more is needed than 3D visualisation
                      and rendering of the cytoarchitecture in a few selected
                      cases. First, the throughput needs to be increased to cover
                      a statistically relevant number of samples. Second, the
                      cytoarchitecture has to be quantified in terms of
                      morphometric parameters, independent of visual impression.
                      Third, dimensionality reduction and classification are
                      required for identification of effects and interpretation of
                      results. To address these challenges, we here design and
                      implement a novel integrated and high throughput XPCT
                      imaging and analysis workflow for 3D histology,
                      pathohistology and drug testing. Our approach uses
                      semi-automated data acquisition, reconstruction and
                      statistical quantification. We demonstrate its capability
                      for the example of lung pathohistology in Covid-19. Using a
                      small animal model, different Covid-19 drug candidates are
                      administered after infection and tested in view of
                      restoration of the physiological cytoarchitecture,
                      specifically the alveolar morphology. To this end, we then
                      use morphometric parameter determination followed by a
                      dimensionality reduction and classification based on optimal
                      transport. This approach allows efficient discrimination
                      between physiological and pathological lung structure,
                      thereby providing quantitative insights into the
                      pathological progression and partial recovery due to drug
                      treatment. Finally, we stress that the XPCT image chain
                      implemented here only used synchrotron radiation for
                      validation, while the data used for analysis was recorded
                      with laboratory CT radiation, more easily accessible for
                      pre-clinical research.},
      cin          = {DOOR ; HAS-User},
      ddc          = {600},
      cid          = {I:(DE-H253)HAS-User-20120731},
      pnm          = {6G3 - PETRA III (DESY) (POF4-6G3) / DFG project 390729940 -
                      EXC 2067: Multiscale Bioimaging: Von molekularen Maschinen
                      zu Netzwerken erregbarer Zellen (390729940) / SFB 1456 A03 -
                      Dimensionalitätsreduktion und Regression im
                      Wasserstein-Raum für quantitative 3D-Histologie (A03)
                      (456837373)},
      pid          = {G:(DE-HGF)POF4-6G3 / G:(GEPRIS)390729940 /
                      G:(GEPRIS)456837373},
      experiment   = {EXP:(DE-H253)P-P10-20150101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38811688},
      UT           = {WOS:001235693100105},
      doi          = {10.1038/s41598-024-61746-4},
      url          = {https://bib-pubdb1.desy.de/record/607553},
}