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Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis

INTRODUCTION: Pulmonary fibrosis is a consequential complication of microbial infections, which has notably been observed in SARS-CoV-2 infections in recent times. Macrophage polarization, specifically the M2-type, is a significant mechanism that induces pulmonary fibrosis, and its role in the devel...

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Autores principales: Chen, Yajie, He, Henghui, Luo, Licheng, Liu, Kangyi, Jiang, Min, Li, Shiqi, Zhang, Xianqi, Yang, Xin, Liu, Qian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076525/
https://www.ncbi.nlm.nih.gov/pubmed/37032846
http://dx.doi.org/10.3389/fmicb.2023.1176339
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author Chen, Yajie
He, Henghui
Luo, Licheng
Liu, Kangyi
Jiang, Min
Li, Shiqi
Zhang, Xianqi
Yang, Xin
Liu, Qian
author_facet Chen, Yajie
He, Henghui
Luo, Licheng
Liu, Kangyi
Jiang, Min
Li, Shiqi
Zhang, Xianqi
Yang, Xin
Liu, Qian
author_sort Chen, Yajie
collection PubMed
description INTRODUCTION: Pulmonary fibrosis is a consequential complication of microbial infections, which has notably been observed in SARS-CoV-2 infections in recent times. Macrophage polarization, specifically the M2-type, is a significant mechanism that induces pulmonary fibrosis, and its role in the development of Post- COVID-19 Pulmonary Fibrosis is worth investigating. While pathological examination is the gold standard for studying pulmonary fibrosis, manual review is subject to limitations. In light of this, we have constructed a novel method that utilizes artificial intelligence techniques to analyze fibro-pathological images. This method involves image registration, cropping, fibrosis degree classification, cell counting and calibration, and it has been utilized to analyze microscopic images of COVID-19 lung tissue. METHODS: Our approach combines the Transformer network with ResNet for fibrosis degree classification, leading to a significant improvement over the use of ResNet or Transformer individually. Furthermore, we employ semi-supervised learning which utilize both labeled and unlabeled data to enhance the ability of the classification network in analyzing complex samples. To facilitate cell counting, we applied the Trimap method to localize target cells. To further improve the accuracy of the counting results, we utilized an effective area calibration method that better reflects the positive density of target cells. RESULTS: The image analysis method developed in this paper allows for standardization, precision, and staging of pulmonary fibrosis. Analysis of microscopic images of COVID-19 lung tissue revealed a significant number of macrophage aggregates, among which the number of M2-type macrophages was proportional to the degree of fibrosis. DISCUSSION: The image analysis method provids a more standardized approach and more accurate data for correlation studies on the degree of pulmonary fibrosis. This advancement can assist in the treatment and prevention of pulmonary fibrosis. And M2-type macrophage polarization is a critical mechanism that affects pulmonary fibrosis, and its specific molecular mechanism warrants further exploration.
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spelling pubmed-100765252023-04-07 Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis Chen, Yajie He, Henghui Luo, Licheng Liu, Kangyi Jiang, Min Li, Shiqi Zhang, Xianqi Yang, Xin Liu, Qian Front Microbiol Microbiology INTRODUCTION: Pulmonary fibrosis is a consequential complication of microbial infections, which has notably been observed in SARS-CoV-2 infections in recent times. Macrophage polarization, specifically the M2-type, is a significant mechanism that induces pulmonary fibrosis, and its role in the development of Post- COVID-19 Pulmonary Fibrosis is worth investigating. While pathological examination is the gold standard for studying pulmonary fibrosis, manual review is subject to limitations. In light of this, we have constructed a novel method that utilizes artificial intelligence techniques to analyze fibro-pathological images. This method involves image registration, cropping, fibrosis degree classification, cell counting and calibration, and it has been utilized to analyze microscopic images of COVID-19 lung tissue. METHODS: Our approach combines the Transformer network with ResNet for fibrosis degree classification, leading to a significant improvement over the use of ResNet or Transformer individually. Furthermore, we employ semi-supervised learning which utilize both labeled and unlabeled data to enhance the ability of the classification network in analyzing complex samples. To facilitate cell counting, we applied the Trimap method to localize target cells. To further improve the accuracy of the counting results, we utilized an effective area calibration method that better reflects the positive density of target cells. RESULTS: The image analysis method developed in this paper allows for standardization, precision, and staging of pulmonary fibrosis. Analysis of microscopic images of COVID-19 lung tissue revealed a significant number of macrophage aggregates, among which the number of M2-type macrophages was proportional to the degree of fibrosis. DISCUSSION: The image analysis method provids a more standardized approach and more accurate data for correlation studies on the degree of pulmonary fibrosis. This advancement can assist in the treatment and prevention of pulmonary fibrosis. And M2-type macrophage polarization is a critical mechanism that affects pulmonary fibrosis, and its specific molecular mechanism warrants further exploration. Frontiers Media S.A. 2023-03-23 /pmc/articles/PMC10076525/ /pubmed/37032846 http://dx.doi.org/10.3389/fmicb.2023.1176339 Text en Copyright © 2023 Chen, He, Luo, Liu, Jiang, Li, Zhang, Yang and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Chen, Yajie
He, Henghui
Luo, Licheng
Liu, Kangyi
Jiang, Min
Li, Shiqi
Zhang, Xianqi
Yang, Xin
Liu, Qian
Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis
title Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis
title_full Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis
title_fullStr Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis
title_full_unstemmed Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis
title_short Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis
title_sort studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10076525/
https://www.ncbi.nlm.nih.gov/pubmed/37032846
http://dx.doi.org/10.3389/fmicb.2023.1176339
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