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Immunohistochemical identification of ACE-2 (SARS-COV II entry mechanism) in the gastrointestinal tract, kidney and lung of rhesus monkeys (Macaca mulatta) and squirrel monkeys (Saimiri sciureus).
Tissue Cell
December 2024
Laboratory of Teaching and Research in Histology and Comparative Embryology (LEPHEC), Biomedical Institute, Fluminense Federal University, Niterói, RJ CEP 24210-130, Brazil. Electronic address:
SARS-Cov-2 is a corona virus that causes COVID-19 disease, a viral infection responsible for the pandemic decreed by the World Health Organization in March 2020. Angiotensin-converting enzyme 2 (ACE-2) functions as the main receptor for SARS-Cov-2. The study aimed to detect the expression of ACE-2 in the gastrointestinal tract, kidney, and lung in the rhesus monkeys and squirrel monkeys.
View Article and Find Full Text PDFImage analysis-based identification of high risk ER-positive, HER2-negative breast cancers.
Breast Cancer Res
December 2024
Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
Article Synopsis
- Researchers focused on breast cancer subtypes Luminal A and Luminal B, using machine learning to analyze H&E images, aiming to identify tumor characteristics linked to higher recurrence risks.
- The study involved training models on segmented images of tumors, finding that an image-based protocol effectively predicted recurrence times, comparable to traditional genomic testing methods (PAM50).
- Results indicated that while adjusting for tumor grade didn't significantly improve subtype prediction, the image analysis provided a viable alternative in identifying patients in need of genomic testing, potentially increasing testing rates among ER+/HER2-patients.
A feasibility study using quantitative and interpretable histological analyses of celiac disease for automated cell type and tissue area classification.
Sci Rep
December 2024
Eli Lilly and Company, Indianapolis, IN, USA.
Histological assessment is essential for the diagnosis and management of celiac disease. Current scoring systems, including modified Marsh (Marsh-Oberhuber) score, lack inter-pathologist agreement. To address this unmet need, we aimed to develop a fully automated, quantitative approach for histology characterisation of celiac disease.
View Article and Find Full Text PDFA semi-permeable insert culture model for the distal part of the nephron with human and mouse tubuloid epithelial cells.
Exp Cell Res
January 2025
Department of Medical BioSciences, Research Institute for Medical Innovation, Radboud University Medical Center, Nijmegen, the Netherlands. Electronic address:
Tubuloids are advanced in vitro models obtained from adult human or mouse kidney cells with great potential for modelling kidney function in health and disease. Here, we developed a polarized human and mouse tubuloid epithelium on cell culture inserts, namely Transwell™ filters, as a model of the distal nephron with an accessible apical and basolateral side that allow for characterization of epithelial properties such as leak-tightness and epithelial resistance. Tubuloids formed a leak-tight and confluent epithelium on Transwells™ and the human tubuloids were differentiated towards the distal part of the nephron.
View Article and Find Full Text PDFArtificial intelligence-based automated determination in breast and colon cancer and distinction between atypical and typical mitosis using a cloud-based platform.
Pathol Oncol Res
November 2024
Department of Pathology, Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
Article Synopsis
- AI technology in pathology relies on supervised machine learning and is increasingly used for tasks like detecting invasive breast cancer and analyzing tissue layers in colorectal specimens.
- The study involved training convolutional neural networks (CNNs) using a large dataset of whole-slide images (WSIs) across different cancer types, achieving notable F1 scores for diagnostic accuracy.
- After extensive training and external validation, the AI models demonstrated a high level of proficiency in identifying tumors and distinguishing between various tissue layers.
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