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Your search keyword '"Karz, A."' showing total 2 results
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2 results on '"Karz, A."'

1. Are You Checked Out at Work?

Author

Horan, Rebecca and Karz, Melissa

Subjects
BUSINESS networks, BUSINESS schools, INTERNET forums, CORPORATE culture, WORKWEEK
Abstract

The article "Are You Checked Out at Work?" discusses strategies to combat disengagement in the workplace, which affects over half of U.S. workers. The authors emphasize the importance of purposeful interactions to reignite engagement, highlighting the benefits of collaboration, brainstorming sessions, seeking peer input, stepping away from the desk, and seeking out community. By implementing these small actions, individuals can break the withdrawal cycle, increase connection, and restore energy and creativity in their work. The article provides practical tips for individuals to re-engage at work and emphasizes the significance of leaning into connection to combat isolation. [Extracted from the article]

Published
2025

2. MetFinder: A Tool for Automated Quantitation of Metastatic Burden in Histological Sections From Preclinical Models.

Author

Karz A, Coudray N, Bayraktar E, Galbraith K, Jour G, Shadaloey AAS, Eskow N, Rubanov A, Navarro M, Moubarak R, Baptiste G, Levinson G, Mezzano V, Alu M, Loomis C, Lima D, Rubens A, Jilaveanu L, Tsirigos A, and Hernando E

Subjects
Animals, Mice, Melanoma pathology, Melanoma metabolism, Tumor Burden, Disease Models, Animal, Algorithms, Image Processing, Computer-Assisted methods, Humans, Automation, Cell Line, Tumor, Skin Neoplasms pathology, Skin Neoplasms metabolism, Software, Neoplasm Metastasis
Abstract

As efforts to study the mechanisms of melanoma metastasis and novel therapeutic approaches multiply, researchers need accurate, high-throughput methods to evaluate the effects on tumor burden resulting from specific interventions. We show that automated quantification of tumor content from whole slide images is a compelling solution to assess in vivo experiments. In order to increase the outflow of data collection from preclinical studies, we assembled a large dataset with annotations and trained a deep neural network for the quantitative analysis of melanoma tumor content on histopathological sections of murine models. After assessing its performance in segmenting these images, the tool obtained consistent results with an orthogonal method (bioluminescence) of measuring metastasis in an experimental setting. This AI-based algorithm, made freely available to academic laboratories through a web-interface called MetFinder, promises to become an asset for melanoma researchers and pathologists interested in accurate, quantitative assessment of metastasis burden., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Published
2025
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