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      Evaluation of intercellular lipid lamellae in the stratum corneum by polarized microscopy

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          Abstract

          Background

          Intercellular lipids contain a lamellar structure that glows in polarized images. It could be expected that the intercellular lipid content be estimated from the luminance values calculated from polarized images of stratum corneum strips. Therefore, we attempted to develop a method for simple and rapid evaluation of the intercellular lipid content through a procedure. Herein, we demonstrated a relationship between the luminance value and the amount of ceramides, one of the main components of intercellular lipids.

          Materials and methods

          The stratum corneum was collected from the forearm using slides with a pure rubber‐based adhesive, which did not produce unnecessary luminescence under polarizing conditions. Images were analyzed using luminance indices. The positive secondary ion peak images were obtained using the time of flight‐secondary ion mass spectrometry; the polarized and brightfield images were obtained using a polarized microscope. The ceramide and protein amount was measured by high‐performance liquid chromatography and bicinchoninic acid protein assay after microscope imaging. Images and quantitative values were used to construct evaluation models based on a convolutional neural network (CNN).

          Results

          There was a correlation between the highlighted areas of the polarized image to overlap with the area where ceramide‐derived peak was detected. Evaluation of the CNN‐based model of the polarized images predicted the amount of ceramides per unit of stratum corneum.

          Conclusion

          The method proposed in the study enabled a large number of specimens to provide a simple, rapid, and efficient evaluation of the intercellular lipid content.

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          Most cited references29

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          ImageNet classification with deep convolutional neural networks

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            ImageNet Large Scale Visual Recognition Challenge

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              Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

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                Author and article information

                Journal
                Skin Res Technol
                Skin Res Technol
                10.1111/(ISSN)1600-0846
                SRT
                Skin Research and Technology
                John Wiley and Sons Inc. (Hoboken )
                0909-752X
                1600-0846
                09 November 2021
                May 2022
                : 28
                : 3 ( doiID: 10.1111/srt.v28.3 )
                : 391-401
                Affiliations
                [ 1 ] KOBAYASHI Pharmaceutical, Co. Ltd. Ibaraki Japan
                [ 2 ] Department of Science and Technology, NARA Institute of Science and Technology Ikoma Japan
                [ 3 ] Data Science Center, NARA Institute of Science and Technology Ikoma Japan
                Author information
                https://orcid.org/0000-0002-5702-3452
                https://orcid.org/0000-0002-4746-3163
                https://orcid.org/0000-0002-5947-6586
                https://orcid.org/0000-0002-3583-203X
                https://orcid.org/0000-0002-6625-3196
                https://orcid.org/0000-0003-3472-9484
                https://orcid.org/0000-0002-7722-055X
                https://orcid.org/0000-0002-2147-6900
                https://orcid.org/0000-0003-4184-9871
                Article
                SRT13109
                10.1111/srt.13109
                9907717
                34751451
                9b0c283c-78b8-412a-beb4-d9f853b8d97e
                © 2021 KOBAYASHI Pharmaceutical Co., Ltd. Skin Research and Technology published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 02 August 2021
                : 25 September 2021
                Page count
                Figures: 8, Tables: 0, Pages: 11, Words: 5761
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                May 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.5 mode:remove_FC converted:06.02.2023

                computer modeling,convolutional neural network,polarized microscope image,skin barrier,skin physiology/structure

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