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Lymphoma in Psittacine Birds: A Histological and Immunohistochemical Assessment.

Daniel J GibsonNicole M NemethHugues BeaufrereCsaba VargaMichael M GarnerLeonardo Susta
Published in: Veterinary pathology (2021)
In psittacine birds, round cell neoplasms that originate from lymphocytes, plasma cells, histiocytes, or mast cells are sporadic and poorly described. The lack of morphological and immunohistochemical diagnostic criteria or grading schemes make specific diagnoses and prognoses challenging. We assessed cases of psittacine birds diagnosed with round cell neoplasia from 3 North American veterinary diagnostic laboratories to describe the diagnostic features of these tumors. For all cases, demographic data, anatomic distribution, histological features, and immunoreactivity for T (CD3) and B (Pax5 and MUM-1) cell markers were assessed using tissue microarrays and whole slide mounts. Thirty-eight psittacine birds representing 14 species were included. Tumors were mainly infiltrative and multicentric, were composed of homogenous sheets of round to polygonal cells, and commonly presented with a high mitotic count (average 21 mitoses per high-power field). Based on Pax5 immunoreactivity, B-cell lymphoma was most common (19/38 [50%]), and was significantly associated with involvement of the gastrointestinal and urogenital systems. Of the 38 cases, 6 (16%) were consistent with T-cell lymphoma, 3 (8%) with plasma cell tumor, and 3 (8%) were double-reactive for both B- and T-lymphocyte markers. This is the first study to describe morphologic and immunohistochemical features of round cell neoplasia in a large number of psittacine birds, and provides benchmark data for future studies aimed at elucidating the diagnosis and prognosis of these neoplasms. These data also provide useful information about reactivity of commercially available antibodies as lymphocyte markers in tissues of multiple psittacine species.
Keyphrases
  • single cell
  • cell therapy
  • induced apoptosis
  • peripheral blood
  • stem cells
  • oxidative stress
  • mesenchymal stem cells
  • cell proliferation
  • machine learning
  • social media
  • data analysis
  • late onset