Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963–0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count.
Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophages are classified depending on the degree of cytoplasmic hemosiderin content. The current gold standard is manual grading, which is however monotonous and time-consuming. We evaluated state-of-the-art deep learning-based methods for single cell macrophage classification and compared them against the performance of nine cytology experts and evaluated inter-and intra-observer variability. Additionally, we evaluated object detection methods on a novel data set of 17 completely annotated cytology whole slide images (WSI) containing 78,047 hemosiderophages. Our deep learning-based approach reached a concordance of 0.85, partially exceeding human expert concordance (0.68 to 0.86, mean of 0.73, SD of 0.04). Intra-observer variability was high (0.68 to 0.88) and inter-observer concordance was moderate (Fleiss' kappa = 0.67). Our object detection approach has a mean average precision of 0.66 over the five classes from the whole slide gigapixel image and a computation time of below two minutes. To mitigate the high inter-and intrarater variability, we propose our automated object detection pipeline, enabling accurate, reproducible and quick EIPH scoring in WSI. Patients with pulmonary hemorrhage (P-Hem) suffer from repeated bleeding into the lungs, which can result in dyspnea and if untreated, may have life threatening consequences 1. There are various causes which lead to P-Hem, including drug abuse, premature birth, leukaemia, autoimmune disorders and immunodeficiencies 2-6. In this paper, we focus on a special subtype of P-Hem called exercise-induced pulmonary hemorrhage (EIPH) in horses. Although EIPH also affects healthy human athletes 7 and racing greyhounds 8 , it is diagnosed most commonly in racing horses and causes reduced athletic performance 9-12. The gold standard for diagnosis of P-Hem in humans and equine animals is to perform cytology of bronchoalveolar lavage fluid (BALF) 4,13 using a scoring system as explained by Golde et al. 4. The red blood cells of the bleeding are degraded into an iron-storage complex called hemosiderin by alveolar macrophages. Hemosiderin-laden macrophages are called hemosiderophages. Prior to microscopic evaluation, the cells are extracted by the BALF procedure and stained with Perlss' Prussian Blue 14 or Turnbull's Blue 15 in order to visualise the iron pigments contained in the hemosiderin. According to the commonly used scoring system (macrophages hemosiderin score) by Golde et al. 4 , alveolar macrophages can be distinguished into five grades depending on their hemosiderin content. This scoring system is based on the principle that a higher score correlates with increased alveolar bleeding 16 .
West Nile virus (WNV), a zoonotic arbovirus, is a new epizootic disease in Germany and caused increasing avian and equine mortality since its first detection in 2018.The northern goshawk (Accipiter gentilis) is highly susceptible to fatal WNV disease and thus is considered as an indicator species for WNV emergence in European countries. Therefore, information regarding clinical presentation and pathological findings is important for identifying suspect cases and initiating further virological diagnostics. Between July and September 2019, ten free-ranging goshawks were admitted to the Small Animal Clinic of the Freie Universität Berlin with later confirmed WNV infection. Clinical, pathological and virological findings are summarized in this report.All birds were presented obtunded and in poor to cachectic body condition. Most of the birds were juveniles (8/10) and females (9/10). Neurologic abnormalities were observed in all birds and included stupor (3/10), seizures (3/10), head tremor (2/10), head tilt (2/10), ataxia (2/10) and monoplegia (2/10). Concurrent diseases like aerosacculitis/pneumonia (7/10), clinical infections with Eucoleus spp. and Trichomonas spp. (3/10), trauma-related injuries (3/10) and myiasis (2/10) were found. Blood analysis results were unspecific considering concurrent diseases. Median time of survival was two days. The most common pathological findings were meningoencephalitis (9/10), myocarditis (8/10), iridocyclitis (8/8) and myositis (7/10). WNV infection was diagnosed by real-time quantitative reverse transcription polymerase chain reaction and confirmed by serology and immunohistochemistry.
Species-specific diversities are particular features of mammalian chloride channel regulator, calcium activated (CLCA) genes. In contrast to four complex gene clusters in mammals, only two CLCA genes appear to exist in chickens. CLCA2 is conserved in both, while only the galline CLCA1 (gCLCA1) displays close genetic distance to mammalian clusters 1, 3 and 4. In this study, sequence analyses and biochemical characterizations revealed that gCLCA1 as a putative avian prototype shares common protein domains and processing features with all mammalian CLCA homologues. It has a transmembrane (TM) domain in the carboxy terminal region and its mRNA and protein were detected in the alimentary canal, where the protein was localized in the apical membrane of enterocytes, similar to CLCA4. Both mammals and birds seem to have at least one TM domain containing CLCA protein with complex glycosylation in the apical membrane of enterocytes. However, some characteristic features of mammalian CLCA1 and 3 including entire protein secretion and expression in cell types other than enterocytes seem to be dispensable for chicken. Phylogenetic analyses including twelve bird species revealed that avian CLCA1 and mammalian CLCA3 form clades separate from a major branch containing mammalian CLCA1 and 4. Overall, our data suggest that gCLCA1 and mammalian CLCA clusters 1, 3 and 4 stem from a common ancestor which underwent complex gene diversification in mammals but not in birds.
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