ArticleYonago acta medica2023
Imaging of Liver Tissues Using a Combination of Silver Impregnation and Low-Vacuum Scanning Electron Microscopy; A Simple Method for the High-Resolution Visualization of Reticulin Structures with Applicability to a Quantitative Analysis.
Article in Yonago acta medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Low-vacuum scanning electron microscopy (LVSEM) enables the detailed three-dimensional imaging of archival tissues without special pretreatments. The clinical utility of LVSEM in the assessment of liver diseases has not yet been defined. So, we investigated the utility of LVSEM imaging in morphological assessments of normal and diseased liver tissues, with a focus on reticulin structures. Methods: Formalin-fixed tissue samples of two normal livers and two hepatocellular carcinomas with background regenerative nodules/areas were stained with platinum blue stain or silver-impregnated using Watanabe's method and then comparatively observed under LVSEM. We also evaluated the applicability of LVSEM imaging of liver tissues to a quantitative analysis using a digital image analysis technique. Results: Optimal high-resolution images of reticulin structures were obtained using 10-μm-thick silver-impregnated sections. Reticulin fibers were clearly observed to run dendritically around sinusoids in normal livers, and markedly increased in regenerative nodules/areas. Normal reticulin frameworks were lost in hepatocellular carcinoma, leaving a few fragments of reticulin fibers within tumors. Moreover, when a quantitative analysis was applied to these images, we successfully demonstrated a significantly higher reticulin fiber density in regenerative nodules/areas than in the normal liver ( Conclusion: We not only obtained detailed three-dimensional images of reticulin structures in various liver tissues by LVSEM combined with silver impregnation but also showed their applicability to a quantitative analysis. The method presented herein may be applied to future studies for the more accurate diagnosis and better classification/risk stratification of various liver diseases.
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