4 research outputs found

    The generation of glioma organoids and the comparison of two culture methods

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    Abstract Background The intra‐ and inter‐tumoral heterogeneity of gliomas and the complex tumor microenvironment make accurate treatment of gliomas challenging. At present, research on gliomas mainly relies on cell lines, stem cell tumor spheres, and xenotransplantation models. The similarity between traditional tumor models and patients with glioma is very low. Aims In this study, we aimed to address the limitations of traditional tumor models by generating patient‐derived glioma organoids using two methods that summarized the cell diversity, histological features, gene expression, and mutant profiles of their respective parent tumors and assess the feasibility of organoids for personalized treatment. Materials and Methods We compared the organoids generated using two methods through growth analysis, immunohistological analysis, genetic testing, and the establishment of xenograft models. Results Both types of organoids exhibited rapid infiltration when transplanted into the brains of adult immunodeficient mice. However, organoids formed using the microtumor method demonstrated more similar cellular characteristics and tissue structures to the parent tumors. Furthermore, the microtumor method allowed for faster culture times and more convenient operational procedures compared to the Matrigel method. Discussion Patient‐derived glioma organoids, especially those generated through the microtumor method, present a promising avenue for personalized treatment strategies. Their capacity to faithfully mimic the cellular and molecular characteristics of gliomas provides a valuable platform for elucidating tumor biology and evaluating therapeutic modalities. Conclusion The success rates of the Matrigel and microtumor methods were 45.5% and 60.5%, respectively. The microtumor method had a higher success rate, shorter establishment time, more convenient passage and cryopreservation methods, better simulation of the cellular and histological characteristics of the parent tumor, and a high genetic guarantee

    Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer

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    Colonoscopy is the most commonly used tool to screen for colorectal cancer (CRC). Here, the authors develop a deep learning model to perform optical diagnosis of CRC by training on a large data set of white-light colonoscopy images and achieve endoscopist-level performance on three independent datasets
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