a systematic review

Histopathology has initially been and is still used to diagnose infectious, degenerative or neoplastic diseases in humans or animals. In addition to qualitative diagnoses semiquantitative scoring of a lesion`s magnitude on an ordinal scale is a commonly demanded task for histopathologists. Multiparametric, semiquantitative scoring systems for mouse models histopathology are a common approach to handle these questions and to include histopathologic information in biomedical research. Inclusion criteria for scoring systems were a first description of a multiparametric, semiquantiative scoring systems which comprehensibly describe an approach to evaluate morphologic lesion. A comprehensive literature search using these criteria identified 153 originally designed semiquantitative scoring systems for the analysis of morphologic changes in mouse models covering almost all organs systems and a wide variety of disease models. Of these, colitis, experimental autoimmune encephalitis, lupus nephritis and collagen induced osteoarthritis colitis were the disease models with the largest number of different scoring systems. Closer analysis of the identified scoring systems revealed a lack of a rationale for the selection of the scoring parameters or a correlation between scoring parameter value and the magnitude of the clinical symptoms in most studies. Although a decision for a particular scoring system is clearly dependent on the respective scientific question this review gives an overview on currently available systems and may therefore allow for a better choice for the respective project

Augmented Mitotic Cell Count using Field Of Interest Proposal

Histopathological prognostication of neoplasia including most tumor grading systems are based upon a number of criteria. Probably the most important is the number of mitotic figures which are most commonly determined as the mitotic count (MC), i.e. number of mitotic figures within 10 consecutive high power fields. Often the area with the highest mitotic activity is to be selected for the MC. However, since mitotic activity is not known in advance, an arbitrary choice of this region is considered one important cause for high variability in the prognostication and grading. In this work, we present an algorithmic approach that first calculates a mitotic cell map based upon a deep convolutional network. This map is in a second step used to construct a mitotic activity estimate. Lastly, we select the image segment representing the size of ten high power fields with the overall highest mitotic activity as a region proposal for an expert MC determination. We evaluate the approach using a dataset of 32 completely annotated whole slide images, where 22 were used for training of the network and 10 for test. We find a correlation of r=0.936 in mitotic count estimate.Comment: 6 pages, submitted to BVM 2019 (bvm-workshop.org

What Is Possible and What Questions Can Be Asked?

In recent years several technologies for the complete analysis of the transcriptome and proteome have reached a technological level which allows their routine application as scientific tools. The principle of these methods is the identification and quantification of up to ten thousands of RNA and proteins species in a tissue, in contrast to the sequential analysis of conventional methods such as PCR and Western blotting. Due to their technical progress transcriptome and proteome analyses are becoming increasingly relevant in all fields of biological research. They are mainly used for the explorative identification of disease associated complex gene expression patterns and thereby set the stage for hypothesis-driven studies. This review gives an overview on the methods currently available for transcriptome analysis, that is, microarrays, Ref-Seq, quantitative PCR arrays and discusses their potentials and limitations. Second, the most powerful current approaches to proteome analysis are introduced, that is, 2D-gel electrophoresis, shotgun proteomics, MudPIT and the diverse technological concepts are reviewed. Finally, experimental strategies for biomarker discovery, experimental settings for the identification of prognostic gene sets and explorative versus hypothesis driven approaches for the elucidation of diseases associated genes and molecular pathways are described and their potential for studies in veterinary research is highlighted

Modulation of the host Th1 immune response in pigeon protozoal encephalitis caused by Sarcocystis calchasi

Pigeon protozoal encephalitis (PPE) is an emerging central-nervous disease of domestic pigeons (Columba livia f. domestica) reported in Germany and the United States. It is caused by the apicomplexan parasite Sarcocystis calchasi which is transmitted by Accipter hawks. In contrast to other members of the Apicomplexa such as Toxoplasma and Plasmodium, the knowledge about the pathophysiology and host manipulation of Sarcocystis is scarce and almost nothing is known about PPE. Here we show by mRNA expression profiling a significant down-modulation of the interleukin (IL)-12/IL-18/interferon (IFN)-γ axis in the brains of experimentally infected pigeons during the schizogonic phase of disease. Concomitantly, no cellular immune response was observed in histopathology while immunohistochemistry and nested PCR detected S. calchasi. In contrast, in the late central-nervous phase, IFN-γ and tumor necrosis factor (TNF) α-related cytokines were significantly up-modulated, which correlated with a prominent MHC-II protein expression in areas of mononuclear cell infiltration and necrosis. The mononuclear cell fraction was mainly composed of T-lymphocytes, fewer macrophages and B-lymphocytes. Surprisingly, the severity and composition of the immune cell response appears unrelated to the infectious dose, although the severity and onset of the central nervous signs clearly was dose-dependent. We identified no or only very few tissue cysts by immunohistochemistry in pigeons with severe encephalitis of which one pigeon repeatedly remained negative by PCR despite severe lesions. Taken together, these observations may suggest an immune evasion strategy of S. calchasi during the early phase and a delayed-type hypersensitivity reaction as cause of the extensive cerebral lesions during the late neurological phase of disease

Administration of Tramadol or Buprenorphine via the drinking water for post-operative analgesia in a mouse-osteotomy model

Adequate analgesia is essential whenever pain might occur in animal experiments. Unfortunately, the selection of suitable analgesics for mice in bone-linked models is limited. Here, we evaluated two analgesics - Tramadol [0.1 mg/ml (Tlow) vs. 1 mg/ml (Thigh)] and Buprenorphine (Bup; 0.009 mg/ml) - after a pre-surgical injection of Buprenorphine, in a mouse-osteotomy model. The aim of this study was to verify the efficacy of these opioids in alleviating pain-related behaviors, to provide evidence for adequate dosages and to examine potential side effects. High concentrations of Tramadol affected water intake, drinking frequency, food intake and body weight negatively in the first 2-3 days post-osteotomy, while home cage activity was comparable between all groups. General wellbeing parameters were strongly influenced by anesthesia and analgesics. Model-specific pain parameters did not indicate more effective pain relief at high concentrations of Tramadol. In addition, ex vivo high-resolution micro computed tomography (µCT) analysis and histology analyzing bone healing outcomes showed no differences between analgesic groups with respect to newly formed mineralized bone, cartilage and vessels. Our results show that high concentrations of Tramadol do not improve pain relief compared to low dosage Tramadol and Buprenorphine, but rather negatively affect animal wellbeing

Malignancy Associated MicroRNA Expression Changes in Canine Mammary Cancer of Different Malignancies

MicroRNA has been suspected to be generally involved in carcinogenesis since their first description. A first study supported this assumption for canine mammary tumors when miRNA expression was compared to normal gland. The present study extends these results by comparing the expression of 16 microRNA (miRNA) and 4 small nucleolar RNA (snoRNA) in tumors of different malignancy, for example, adenomas, nonmetastasizing and metastasizing carcinomas as well as lymph node metastases, with each other and with normal mammary gland. All neoplastic tissues differed in their miR-210 expression levels from normal gland. While metastatic cells differed in their expression of mir-29b, miR-101, mir-125a, miR-143, and miR-145 from primary tumors, the comparison of miRNA expression in primary tumors of different malignancy failed to reveal significant differences except for a significant downregulation of mir-125a in metastasizing carcinomas when compared to adenomas

Predominantly Fibrous Malignant Mesothelioma in a Cat

Malignant mesotheliomas are rare tumours in domestic cats. They occur within the abdominal or thoracic cavity and are regularly associated with pleural or peritoneal effusions. The histopathological diagnosis can be quite challenging, as these neoplasms may resemble other epithelial or mesenchymal neoplasms. However, differentiation can be achieved by immunohistochemistry in most cases. Here we describe the rare case of a malignant mesothelioma of the fibrous subtype in the thoracic cavity of a cat and discuss differential diagnoses and treatment options for this tumor type

A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor

We introduce a novel, large-scale dataset for microscopy cell annotations. The dataset includes 32 whole slide images (WSI) of canine cutaneous mast cell tumors, selected to include both low grade cases as well as high grade cases. The slides have been completely annotated for mitotic figures and we provide secondary annotations for neoplastic mast cells, inflammatory granulocytes, and mitotic figure look-alikes. Additionally to a blinded two-expert manual annotation with consensus, we provide an algorithm-aided dataset, where potentially missed mitotic figures were detected by a deep neural network and subsequently assessed by two human experts. We included 262,481 annotations in total, out of which 44,880 represent mitotic figures. For algorithmic validation, we used a customized RetinaNet approach, followed by a cell classification network. We find F1-Scores of 0.786 and 0.820 for the manually labelled and the algorithm-aided dataset, respectively. The dataset provides, for the first time, WSIs completely annotated for mitotic figures and thus enables assessment of mitosis detection algorithms on complete WSIs as well as region of interest detection algorithms