Challenges and Opportunities for the Veterinary Pathologist in Biomedical Research

A Primer for Research Scientists on Assessing Mouse Gross and Histopathology Images in the Biomedical Literature

Advances in genomic technologies have enabled the development of abundant mouse models of human disease, requiring accurate phenotyping to elucidate the consequences of genetic manipulation. Anatomic pathology, an important component of the mouse phenotyping pipeline, is ideally performed by human or veterinary pathologists; however, due to insufficient numbers of pathologists qualified to assess these mouse models morphologically, research scientists may perform "do-it-yourself" pathology, resulting in diagnostic error. In the biomedical literature, pathology data is commonly presented as images of tissue sections, stained with either hematoxylin and eosin or antibodies via immunohistochemistry, accompanied by a figure legend. Data presented in such images and figure legends may contain inaccuracies. Furthermore, there is limited guidance for non-pathologist research scientists concerning the elements required in an ideal pathology image and figure legend in a research publication. In this overview, the components of an ideal pathology image and figure legend are outlined and comprise image quality, image composition, and image interpretation. Background knowledge is important for producing accurate pathology images and critically assessing these images in the literature. This foundational knowledge includes understanding relevant human and mouse anatomy and histology and, for cancer researchers, an understanding of human and mouse tumor classification and morphology, mouse stain background lesions, and tissue processing artifacts. Accurate interpretation of immunohistochemistry is also vitally important and is detailed with emphasis on the requirement for tissue controls and the distribution, intensity, and intracellular location of staining. Common pitfalls in immunohistochemistry interpretation are outlined, and a checklist of questions is provided by which any pathology image may be critically examined. Collaboration with pathologist colleagues is encouraged. This overview aims to equip researchers to critically assess the quality and accuracy of pathology images in the literature to improve the reliability and reproducibility of published pathology data. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Bridging clinic and wildlife care with AI-powered pan-species computational pathology

Cancers occur across species. Understanding what is consistent and varies across species can provide new insights into cancer initiation and evolution, with significant implications for animal welfare and wildlife conservation. We build a pan-species cancer digital pathology atlas (panspecies.ai) and conduct a pan-species study of computational comparative pathology using a supervised convolutional neural network algorithm trained on human samples. The artificial intelligence algorithm achieves high accuracy in measuring immune response through single-cell classification for two transmissible cancers (canine transmissible venereal tumour, 0.94; Tasmanian devil facial tumour disease, 0.88). In 18 other vertebrate species (mammalia = 11, reptilia = 4, aves = 2, and amphibia = 1), accuracy (range 0.57-0.94) is influenced by cell morphological similarity preserved across different taxonomic groups, tumour sites, and variations in the immune compartment. Furthermore, a spatial immune score based on artificial intelligence and spatial statistics is associated with prognosis in canine melanoma and prostate tumours. A metric, named morphospace overlap, is developed to guide veterinary pathologists towards rational deployment of this technology on new samples. This study provides the foundation and guidelines for transferring artificial intelligence technologies to veterinary pathology based on understanding of morphological conservation, which could vastly accelerate developments in veterinary medicine and comparative oncology.

Animal models for studying covid-19, prevention, and therapy: Pathology and disease phenotypes

Translational models have played an important role in the rapid development of safe and effective vaccines and therapeutic agents for the ongoing coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Animal models recapitulating the clinical and underlying pathological manifestations of COVID-19 have been vital for identification and rational design of safe and effective vaccines and therapies. This manuscript provides an overview of commonly used COVID-19 animal models and the pathologic features of SARS-CoV-2 infection in these models in relation to their clinical presentation in humans. Also discussed are considerations for selecting appropriate animal models for infectious diseases such as COVID-19, the host determinants that can influence species-specific susceptibility to SARS-CoV-2, and the pathogenesis of COVID-19. Finally, the limitations of currently available COVID-19 animal models are highlighted.

Exclusion of Expert Contributors From Authorship Limits the Quality of Scientific Articles

Veterinary pathologists are key contributors to multidisciplinary biomedical research. However, they are occasionally excluded from authorship in published articles despite their substantial intellectual and data contributions. To better understand the potential origins and implications of this practice, we identified and analyzed 29 scientific publications where the contributing pathologist was excluded as an author. The amount of pathologist-generated data contributions were similar to the calculated average contributions for authors, suggesting that the amount of data contributed by the pathologist was not a valid factor for their exclusion from authorship. We then studied publications with pathologist-generated contributions to compare the effects of inclusion or exclusion of the pathologist as an author. Exclusion of the pathologist from authorship was associated with significantly lower markers of rigor and reproducibility compared to articles in which the pathologist was included as author. Although this study did not find justification for the exclusion of pathologists from authorship, potential consequences of their exclusion on data quality were readily detectable.