Publications by authors named "Bertram C"

Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics can improve reproducibility, but current manual methods are time-consuming. The aim of this study was to explore the limitations of estimates and develop alternative morphometric solutions for canine cutaneous mast cell tumors (ccMCTs).

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The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation.

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Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter- and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)-supported MC has been shown to correlate well with traditional MC on glass slides.

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Numerous prognostic factors are currently assessed histologically and immunohistochemically in canine mast cell tumors (MCTs) to evaluate clinical behavior. In addition, polymerase chain reaction (PCR) is often performed to detect internal tandem duplication (ITD) mutations in exon 11 of the gene (-11-ITD) to predict the therapeutic response to tyrosine kinase inhibitors. This project aimed at training deep learning models (DLMs) to identify MCTs with -11-ITD solely based on morphology.

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Article Synopsis
  • * Traditional methods of counting lymphocytes in intestinal biopsies have low reliability due to varying opinions among pathologists, prompting the development of an AI model for more consistent detection.
  • * The AI model shows high sensitivity and predictive value in identifying lymphocytes, potentially improving diagnostic accuracy for feline chronic enteropathy when supervised by a pathologist, despite some errors noted in specific cases.
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  • Neoplasia is prevalent in guinea pigs, with a study showing a tumor incidence of 20.5% in a large population of 2,474 autopsy cases.
  • The most common tumors identified were lymphomas or leukemias, affecting 7.0% of the guinea pigs, and these tumors often spread to multiple organs.
  • There was a notable increase in tumor prevalence with age, rising from 1.4% in those under 0.5 years to 53.6% in guinea pigs older than 5 years, highlighting the need for further research on tumor characteristics in these animals.
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  • Feline eosinophilic sclerosing fibroplasia (FESF) is a rare inflammatory disease in cats that affects the gastrointestinal tract and can resemble tumors.
  • A case series of 17 cats showed FESF linked with intralesional lymphoma, characterized by specific cell markers (CD56 and/or CD3) indicating a lymphocyte origin.
  • This report introduces a new subtype of lymphoma associated with FESF, suggesting the term "eosinophilic sclerosing lymphoma."
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Neoplastic processes of the mandible and their treatment are rarely reported in large animal species. Specifically, giant cell tumor of bone is an uncommon tumor in animals and has been associated in humans with locally invasive behavior and a high recurrence rate. En-bloc resection is the treatment of choice, but depending on the localization of the tumor, this may result in functional deficits.

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Objective: Intravascular lymphatic valves often occur in proximity to vessel junctions. It is commonly held that disturbed flow at junctions is responsible for accumulation of valve-forming cells (VFCs) at these locations as the initial step in valve creation, and the one which explains the association with these sites. However, evidence in favor is largely limited to cell culture experiments.

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The integration of deep learning-based tools into diagnostic workflows is increasingly prevalent due to their efficiency and reproducibility in various settings. We investigated the utility of automated nuclear morphometry for assessing nuclear pleomorphism (NP), a criterion of malignancy in the current grading system in canine pulmonary carcinoma (cPC), and its prognostic implications. We developed a deep learning-based algorithm for evaluating NP (variation in size, i.

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The ability of human melanoma cells to switch from an epithelial to a mesenchymal phenotype contributes to the metastatic potential of disease. Metalloproteinases (MPs) are crucially involved in this process by promoting the detachment of tumor cells from the primary lesion and their migration to the vasculature. In gray horse melanoma, epithelial-mesenchymal transition (EMT) is poorly understood, prompting us to address MP expression in lesions versus intact skin by transcriptome analyses and the immunofluorescence staining (IF) of gray horse tumor tissue and primary melanoma cells.

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Digitalization of pathology workflows has undergone a rapid evolution and has been widely established in the diagnostic field but remains a challenge in the nonclinical safety context due to lack of regulatory guidance and validation experience for good laboratory practice (GLP) use. One means to demonstrate that digital slides are fit for purpose, that is, provide sufficient quality for pathologists to reach a diagnosis, is conduction of comparison studies, which have been published both, for veterinary and human diagnostic pathology, but not for toxicologic pathology. Here, we present an approach that uses study material from nonclinical safety studies and that allows for the statistical comparison of concordance rates for glass and digital slide evaluation while minimizing time and effort for the involved personnel.

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Synovial myxoma, a rare joint tumor in dogs, has traditionally been considered benign, acknowledging that local invasion into regional tissues including bone may be present. Given the diagnostic challenges in distinguishing synovial myxoma from other joint lesions through clinical features and diagnostic imaging, definitive diagnosis relies on characteristic gross and histologic features. Within the inner surface of the joint capsule, synovial myxomas form nodules of stellate-to-spindle cells within abundant myxomatous matrix.

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A previously developed model of a lymphatic vessel as a chain of lymphangions was investigated to determine whether lymphangions of unequal length reduce pumping relative to a similar chain of equal-length ones. The model incorporates passive elastic and active contractile properties taken from ex vivo measurements, and intravascular lymphatic valves as transvalvular pressure-dependent resistances to flow with hysteresis and transmural pressure-dependent bias to the open state as observed experimentally. Coordination of lymphangion contractions is managed by marrying an autonomous transmural pressure-dependent pacemaker for each lymphangion with bidirectional transmission of activation signals between lymphangions, qualitatively matching empirical observations.

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Lactate has received attention as a potential therapeutic intervention for brain diseases, particularly those including energy deficit, exacerbated inflammation, and disrupted redox status, such as cerebral ischemia. However, lactate roles in metabolic or signaling pathways in neural cells remain elusive in the hypoxic and ischemic contexts. Here, we tested the effects of lactate on the survival of a microglial (BV-2) and a neuronal (SH-SY5Y) cell lines during oxygen and glucose deprivation (OGD) or OGD followed by reoxygenation (OGD/R).

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Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This task is challenging for algorithms and human experts alike, with deterioration of algorithmic performance under shifts in image representations. Considerable covariate shifts occur when assessment is performed on different tumor types, images are acquired using different digitization devices, or specimens are produced in different laboratories.

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One of the most relevant prognostic indices for tumors is cellular proliferation, which is most commonly measured by the mitotic activity in routine tumor sections. The goal of this systematic review was to analyze the methods and prognostic relevance of histologically measuring mitotic activity that have been reported for canine tumors in the literature. A total of 137 articles that correlated the mitotic activity in canine tumors with patient outcome were identified through a systematic (PubMed and Scopus) and nonsystematic (Google Scholar) literature search and eligibility screening process.

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Increased proliferation is a driver of tumorigenesis, and quantification of mitotic activity is a standard task for prognostication. This systematic review is an analysis of all available references on mitotic activity in feline tumors to provide an overview of the assessment methods and prognostic value. A systematic literature search in PubMed and Scopus and a nonsystematic search in Google Scholar were conducted.

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Background: Clinical manifestation of prostate cancer (PCa) is highly variable. Aggressive tumors require radical treatment while clinically non-significant ones may be suitable for active surveillance. We previously developed the prognostic ProstaTrend RNA signature based on transcriptome-wide microarray and RNA-sequencing (RNA-Seq) analyses, primarily of prostatectomy specimens.

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Cell division through mitosis (microscopically visible as mitotic figures, MFs) is a highly regulated process. However, neoplastic cells may exhibit errors in chromosome segregation (microscopically visible as atypical mitotic figures, AMFs) resulting in aberrant chromosome structures. AMFs have been shown to be of prognostic relevance for some neoplasms in humans but not in animals.

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Histopathological examination of tissue samples is essential for identifying tumor malignancy and the diagnosis of different types of tumor. In the case of lymphoma classification, nuclear size of the neoplastic lymphocytes is one of the key features to differentiate the different subtypes. Based on the combination of artificial intelligence and advanced image processing, we provide a workflow for the classification of lymphoma with regards to their nuclear size (small, intermediate, and large).

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