Nuclear pleomorphism in canine cutaneous mast cell tumors: Comparison of reproducibility and prognostic relevance between estimates, manual morphometry, and algorithmic morphometry.

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).

Automated Nuclear Morphometry: A Deep Learning Approach for Prognostication in Canine Pulmonary Carcinoma to Enhance Reproducibility.

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.

Histological classification of canine and feline lymphoma using a modular approach based on deep learning and advanced image processing.

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).

Colloidally prepared Pt nanowires versus impregnated Pt nanoparticles: comparison of adsorption and reaction properties.

Ligand-capped Pt nanowires, prepared by colloidal synthesis and deposited on a high surface area γ-Al(2)O(3) support, were subjected to surface characterization by electron microscopy and FTIR spectroscopy using CO as a probe molecule. The structural, adsorption, and catalytic reaction properties of the colloidal Pt nanowires were compared to those of conventional, impregnated Pt nanoparticles on the same Al(2)O(3) support. In situ FTIR spectroscopy indicated ligand effects on the CO resonance frequency, irreversible CO-induced surface roughening upon CO adsorption, and a higher resistance of colloidal catalysts toward oxidation (both in oxygen and during CO oxidation), suggesting that the organic ligands might protect the Pt surface.