Mast cell tumors are uncommon in horses and typically have a benign clinical course, but there are occasional reports of more aggressive behavior. The objective of this study was to review histologic features and KIT expression patterns of 72 previously diagnosed equine cutaneous mast cell tumors to determine if either is associated with clinical outcomes. Biopsy specimens were reviewed using histologic criteria derived from grading schemes, and KIT antibody expression patterns used in canine tumors and surveys were sent to referring veterinarians for follow-up clinical data. Arabians were overrepresented relative to the reference population. Most tumors were well differentiated with low mitotic rates (96%), and aberrant KIT staining patterns, as described in dogs, were uncommonly identified (12%). Associated clinical disease was uncommon and no tumors exhibited malignant behavior. Overall, KIT staining pattern and histologic features were not associated with poor clinical outcome or abnormal tumor behavior.
Histomonas meleagridis is a flagellate protozoan organism that can cause severe necrotizing typhlitis and hepatitis in gallinaceous birds. Peafowl ( Pavo spp.) have been shown to be susceptible to histomoniasis in experimental settings, but there are few reports of natural histomoniasis in this species. A retrospective study of the archived cases at 2 veterinary diagnostic laboratories in the United States yielded 5 cases of peafowl with gross and histologic findings characteristic of histomoniasis. Lesions included bilateral, transmural fibrinonecrotic typhlitis and multifocal necrotizing hepatitis with associated trophozoites morphologically consistent with H. meleagridis. There was no evidence of Heterakis gallinarum infestation in the studied cases. DNA was extracted from formalin-fixed, paraffin-embedded liver and ceca from all 5 cases and was analyzed using multiple sets of primers with subsequent sequencing and genotyping. Four samples were positive for H. meleagridis, and 1 sample was positive for both H. meleagridis and Tetratrichomonas gallinarum. These results confirm that peafowl develop clinical disease similar to that described previously in other gallinaceous birds infected by H. meleagridis. The role of T. gallinarum remains unknown and further research is necessary to elucidate its role, if any, in the pathogenesis of the observed lesions.
Heartland virus (HRTV) is a North American phlebovirus suspected to be transmitted by the lone star tick Amblyomma americanum. White-tailed deer (WTD) have been shown to develop HRTV-neutralizing antibodies following experimental infection. To further define the geographic distribution of HRTV through retrospective sampling of WTD, sera from the WTD herd health serum archive at the Southeastern Cooperative Wildlife Disease Study between 2001 and 2015 were analyzed using serum neutralization. Of 783 serum samples tested, 57 (7.3%) were positive for HRTV-neutralizing antibodies. Deer with moderate to heavy tick burdens were more likely seropositive. Seropositive samples were obtained from deer originating from states with documented human cases of HRTV-associated disease. Seropositive samples were identified from years before the recognition of the first human case in 2009. Overall, this study indicates that WTD in the southeastern United States have been exposed to HRTV as early as 2001 and that the presence of seropositive animals corresponds roughly with reported human HRTV-associated disease.Note: Supplemental tables appear at www.ajtmh.org.
The use of natural language data for animal population surveillance represents a valuable opportunity to gather information about potential disease outbreaks, emerging zoonotic diseases, or bioterrorism threats. In this study, we evaluate machine learning methods for conducting syndromic surveillance using free-text veterinary necropsy reports. We train a system to detect if a necropsy report from the Wisconsin Veterinary Diagnostic Laboratory contains evidence of gastrointestinal, respiratory, or urinary pathology. We evaluate the performance of several machine learning algorithms including deep learning with a long shortterm memory network. Although no single algorithm was superior, random forest using feature vectors of TF-IDF statistics ranked among the top-performing models with F1 scores of 0.923 (gastrointestinal), 0.960 (respiratory), and 0.888 (urinary). This model was applied to over 33,000 necropsy reports and was used to describe temporal and spatial features of diseases within a 14-year period, exposing epidemiological trends and detecting a potential focus of gastrointestinal disease from a single submitting producer in the fall of 2016.
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