Myelolipomas are benign proliferations of well-differentiated adipose and hematopoietic tissue. Myelolipomas are reported to arise in the spleen, liver, and adrenal gland. Splenic myelolipomas rarely result in hemoabdomen or splenic rupture. In dogs and cats, no reports associate the formation of hematomas with splenic myelolipomas. The few published cases of myelolipoma have precluded the determination of breed and sex predisposition. A total of 2697 cases of myelolipomas were retrieved, comprising 96.3% (2596/2697) dogs and 3.7% (101/2697) cats. In dogs, splenic hematoma arising from myelolipoma was reported in 39.8% of the cases (1017/2554) and was a significant risk factor for both hemoabdomen (11.8% vs 2.0%; odds ratio [OR], 6.138; P < 0.001) and splenic rupture (13.9% vs 3.5%; OR, 3.898; P < 0.001). In cats, splenic hematoma, hemoabdomen, and splenic rupture caused by myelolipomas were infrequent, and half of the hepatic myelolipomas arose within peritoneopericardial diaphragmatic hernias (PPDH). In dogs, Labrador retrievers were the most affected breed (13.6%; 354/2596), and the mean age at the time of diagnosis was 11.4 years (range 1-18 years). The mean mass diameter was 7.4 cm (range 0.2-35 cm). The spleen (98.2%; 2550/2596) and adrenal gland (1.3%; 35/2596) were the most common locations. In cats, 53.5% (54/101) were domestic shorthairs, with a mean age of 11.1 years (range 1-18 years). The mean mass size was 2.4 cm in diameter (range 0.2-9 cm). The spleen (85.1%; 86/101) and liver (13.9%; 14/101) were the most common locations.

The coiled-coil domain-containing 85c (Ccdc85c) knockout (KO) rat generated by genome editing exhibits hydrocephalus and subcortical heterotopia. In this study, we aimed to further investigate the function of CCDC85C protein in the development of the retina. Expression of CCDC85C, acetylated tubulin, ciliary rootlet coiled-coil protein (CROCC), zonula occludens-1 (ZO-1), glutamine synthetase, and PAX6 were examined immunohistochemically in wild-type F344 rats at embryonic day (ED) 19 and at postnatal days (PNDs) 0, 4, 6, 13, and 20. Immunoelectron microscopy was performed for CCDC85C in the normal rat retina. Retinal lesions in Ccdc85c KO rats were examined using fundus photography, optical coherence tomography (OCT), and histology. In the normal rat retina, CCDC85C was co-localized with ZO-1 in the outer limiting membrane and persistently expressed after ED19. Ultrastructurally, CCDC85C was located between the outer nuclear layer and the inner segments, and showed the same location as the tight junction. In Ccdc85c KO rats, multifocal retinal dysplasia; disarrangement of the inner and outer segments, cilia, and rootlets; and impaired development of Müller cells were observed. In OCT images, Ccdc85c KO rats showed parallel hyperintense striations in the inner nuclear layer, and low reflectivity of the outer limiting membrane and layer of rods and cones. These results suggest that CCDC85C protein is located in the tight junction complex and is involved in retinal layer formation. The Ccdc85c KO rat model provides a novel tool to study retinal development as well as genetic hydrocephalus.

The advent of artificial intelligence (AI) technologies is creating a paradigm shift in drug discovery and development. Veterinary pathology is an area that can significantly benefit from AI tools. The availability of AI algorithms in commercial software packages such as HALO and Visiopharm has generated interest in automating pathologists' workflows for the detection and quantification of histology endpoints from whole-slide images. As each software package provides a distinct set of AI models, the relative performance and equivalency of these models for lesion detection and quantification are poorly understood. Here, we present a systematic comparison of the performance of AI algorithms developed in HALO and Visiopharm. Specifically, we trained AI algorithms in HALO and Visiopharm by using the same ground truth training data to detect different subregions and quantify their areas from hematoxylin and eosin (HE)-stained images of murine skin sections. We also calculate the performance metrics (precision, recall, F1 score) for each algorithm using the same test dataset. Our analysis shows that both HALO and Visiopharm algorithms have comparable performance and are resilient to training data size and changes in the color profile of the HE images. As an application, we compared the results of the AI algorithms against pathologists' scores to quantify epidermal hyperplasia. Our analysis shows that the quantitative data from AI algorithms are consistent with pathologists' scores. These results provide a quantitative characterization of commercially available AI models in HALO and Visiopharm and offer practical guidelines for designing and validating AI algorithms using these software packages.

The efficacy of candidate drugs for neuroprotection is tested in rodent models of retinal atrophy where light-induced outer retinal atrophy (ORA) is quantified by manual or semi-automated measurements, analyses that are time-consuming and error-prone. We developed a quantitative, automated image analysis-based method of ORA assessment in whole-slide images (WSIs). A commercial, cloud-based, artificial intelligence image analysis platform (Aiforia) was used to train convolutional neural network (CNN)-based deep learning models for semantic segmentation of retina and object counts of outer nuclear layer (ONL) nuclei. Model development was an iterative process of establishing and fine-tuning the ground truth (manual annotations), adding training annotations, and increasing the number of CNN training iterations. Inter-observer concordance yielded F1-scores exceeding 0.98 for retina area and 0.92 for ONL counts. Performance of the developed algorithm in comparison with manual annotations by individual validators yielded F1-scores between 0.89 and 0.95. Analysis of WSIs by the algorithm was completed 50× faster compared with manual analysis on hematoxylin and eosin-stained sections; however, it required time to digitize the slides. Performance of the ORA quantification deep learning model is noninferior to that of current manual ORA scoring approaches and provides improved reproducibility and reduced time and cost of analysis.

Lymphoma is a common cancer in dogs, which often presents as generalized peripheral lymphadenopathy. Fine needle aspiration is frequently used to investigate the cause of peripheral lymphadenopathy, and artificial intelligence technology could potentially assist in cytologic interpretation. In this study, YOLOv11, an open-source object detection algorithm, was evaluated for cell-level identification in canine lymph node cytology to diagnose lymphoma. Cytologic images were captured using 2 smartphones from 11 non-lymphoma and 34 intermediate-to-large B-cell lymphoma peripheral lymph node aspirates in dogs, confirmed by cytologic interpretation plus either flow cytometry or polymerase chain reaction for antigen receptor rearrangements. A total of 25,761 intact cells were annotated across 680 images. Models were trained and validated under 5 cross-device configurations, comparing a 4-label approach (small, intermediate, and large lymphocytes and neutrophils) and a 3-label approach that merged intermediate and large lymphocytes. At the cell level, combining intermediate and large lymphocytes improved cell-level classification. In the optimized configuration, the model achieved an average precision of 87.60%, an average recall of 84.71%, a mean average precision 50 (mAP50) of 89.33%, and an F1 score of 86.02% on the independent test set. At the aspirate level, the model achieved near-perfect performance and correctly inferred diagnoses in all test aspirates by assessing the proportions of intermediate-to-large lymphocytes. These findings demonstrate the potential feasibility of smartphone-based deep learning assistance for veterinary cytology and highlight the importance of prospective, workflow-integrated validation, including external validation, across a broader range of lymph node diseases.

Hemorrhagic necrotizing pneumonia caused by cytotoxic necrotizing factor-1-positive extraintestinal pathogenic Escherichia coli (ExPEC) is a rapidly fatal disease in dogs. Lung tissues from 6 client-owned dogs with culture-confirmed ExPEC pneumonia were analyzed using immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH). Both assays localized E. coli within pneumocytes, alveolar macrophages, and alveolar spaces. Notably, one case exhibited numerous filamentous E. coli, representing a stress-induced morphologic adaptation previously unreported in canine ExPEC pneumonia. Recognizing filamentous E. coli is diagnostically crucial to avoid misidentification as other Gram-negative bacteria. While FISH provided high specificity, IHC offered practical, rapid detection compatible with routine microscopy. Four dogs had recent exposures to high-density facilities (breeding, boarding, or training), suggesting possible environmental transmission. These findings expand the clinicopathologic spectrum of canine ExPEC pneumonia and demonstrate the value of integrating spatial techniques with traditional culture to improve diagnostic accuracy.

The analysis of hematoxylin and eosin (HE)-stained bone marrow (BM) tissue sections in drug development toxicologic pathology studies is a key step in the in vivo safety assessment of new drug candidates. Routine histologic analysis provides critical information about the cellularity and tissue architecture of the BM but only limited insights into the cell lineages that comprise the hematopoietic tissue. The evaluation of BM cell types can be augmented by examining BM smear preparations or using immunohistochemical (IHC) labeling of histologic sections to identify lineages of interest; however, neither of these approaches is included in the standard assessment. In addition, manual evaluation is time-consuming, subject to inter-observer variability, and challenging due to the complexity of BM morphology and architecture. In this project, we developed a deep learning model to predict IHC labeling of BM cell lineages on HE-stained BM tissue sections. The model is trained on an immunohistochemistry-informed, HE-based ground truth for the sequential labeling of CD11b and myeloperoxidase markers and can predict cell segmentation mappings with over 69% agreement with the ground truth, using only HE slides as input. Furthermore, our method can discern cell population changes that reflect qualitative diagnoses identified by pathologists during routine slide interpretation. Our automated method holds promise for enhancing routine pathologist assessments of BM HE slides by extending the evaluation of hematopoietic cell lineages without the need to generate additional samples.

Collagenofibrotic glomerulopathy (CG) is a rare renal disease characterized by the abnormal accumulation of type III collagen within glomeruli. This is the first study to report CG in 3 tortoises housed within the same facility. Histologic analyses revealed eosinophilic glomerular deposits, which had segmental positive immunolabeling with anti-type III collagen antibodies by immunohistochemistry. These deposits were observed in approximately 70% of glomeruli in case 1, 50% in case 2, and 95% in case 3. An ultrastructural examination confirmed that the deposits were comprised of irregularly arranged collagen fibrils with periodic transverse striations mixed with fibrils of a smaller diameter. Additionally, similar deposits were noted in the walls of arterioles in the spleen of case 3. Therefore, CG in tortoises may be a systemic disease.

Testicular tumors are rarely reported in passerine birds and are typically described as isolated cases. This study characterizes the clinical, pathological, immunohistochemical, and epidemiological features of 13 testicular neoplasms diagnosed in ultramarine grosbeaks (Cyanoloxia brissonii; n = 12) and a green-winged saltator (Saltator similis; n = 1), 2 South American passerine species commonly used in singing competitions. Clinical signs included coelomic distension, respiratory distress, pododermatitis, feather loss, cloacal prolapse, and decreased vocalization. Radiography demonstrated displacement of coelomic organs and, in some cases, markedly reduced gastrointestinal transit associated with tumor compression. Grossly, tumors were large, pale tan to yellow, frequently multilobulated, and caused compression of adjacent viscera. Histologically, all neoplasms were classified as sustentacular cell tumors (SCTs) based on established histomorphological criteria adapted for avian testes. Most tumors exhibited a tubular growth pattern (77%), whereas diffuse (15%) and mixed-type (7%) patterns were less common. Rare Call-Exner-like bodies and extensive contralateral testicular atrophy were observed. Immunohistochemically, KIT (CD117) and neuron-specific enolase were the most consistently expressed markers, whereas melan-A, calretinin, and GATA-4 showed sporadic labeling and inhibin-α, vimentin, and pancytokeratin were consistently negative. Liver metastases occurred in 2 ultramarine grosbeaks, demonstrating the malignant potential of SCTs in passerines. This study represents the largest series of testicular tumors reported in passerine birds and provides the first evidence that participation in singing competitions is significantly associated with the development of SCTs in ultramarine grosbeaks. Our results underscore the importance of including testicular neoplasia as a differential diagnosis in cases of coelomic distension in passerines.

Infectious abortion is a major cause of reproductive loss in cattle, yet the diagnosis remains challenging. In this study, we evaluated the gross and microscopic lesions of bovine fetuses aborted due to the main infectious agents in Argentina. Clear pathogen-specific patterns were identified. Neospora caninum was strongly associated with central nervous system (CNS) and cardiac inflammation, whereas Campylobacter fetus and Brucella abortus predominantly produced bronchointerstitial pneumonia. Leptospira spp. consistently affected hepatic and renal tissues and was associated with marked portal hepatitis and interstitial nephritis. Bovine viral diarrhea virus (BVDV) infections resulted in mild multisystemic and nonspecific lesions. Lesion severity also differed by fetal age. N. caninum produced more severe CNS and cardiac inflammation in mid-gestation fetuses, and C. fetus induced more severe hepatic lesions during mid-gestation. Statistical analyses confirmed distinct organ tropisms and lesion profiles among pathogens, supporting their diagnostic relevance. Based on these findings, targeted sampling of CNS and heart (for N. caninum); lung (for C. fetus and B. abortus); liver and kidney (for Leptospira spp.); and heart, lung, and CNS (for BVDV) may improve diagnostic accuracy in abortion investigations. These findings provide robust criteria for differentiating major abortifacient agents in routine diagnostic practice.

Amyloidosis is a group of protein-misfolding disorders characterized by the deposition of insoluble fibrils in tissues. In birds, only systemic amyloid A (AA) amyloidosis and cerebral Aβ amyloidosis have been described to date, and non-AA systemic amyloidosis has not been reported. We describe the first case of non-AA systemic amyloidosis in an avian species, fibrinogen Aα-chain (FibA) amyloidosis, in an 8-year-old male blue-winged kookaburra (Dacelo leachii). Histologically, prominent amyloid deposits were observed in renal glomeruli and in arterial walls of multiple organs. Ultrastructurally, amyloid deposits consisted of non-branching fibrils. Amyloid isolated from multiple organs was consistently identified as FibA-derived by liquid chromatography-tandem mass spectrometry using a customized protein database constructed from hepatic transcriptomic data, enabling amyloid typing in a species lacking comprehensive genomic resources. Peptide mapping demonstrated predominant involvement of the FibA αC-connector region, which was further supported by immunohistochemical labeling of amyloid deposits with an antibody against this region. This case expands the spectrum of avian amyloidosis and highlights the evolutionarily conserved amyloidogenic vulnerability of FibA across vertebrate species.