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Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology
Endometriosis is a chronic inflammatory hormone-dependent condition associated with pelvic pain and infertility, characterized by the growth of ectopic endometrium outside the uterus. Given its still unknown etiology, treatments usually aim at diminishing pain and/or achieving pregnancy. Despite som...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425005/ https://www.ncbi.nlm.nih.gov/pubmed/32787880 http://dx.doi.org/10.1186/s12967-020-02471-0 |
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author | Malvezzi, Helena Marengo, Eliana Blini Podgaec, Sérgio Piccinato, Carla de Azevedo |
author_facet | Malvezzi, Helena Marengo, Eliana Blini Podgaec, Sérgio Piccinato, Carla de Azevedo |
author_sort | Malvezzi, Helena |
collection | PubMed |
description | Endometriosis is a chronic inflammatory hormone-dependent condition associated with pelvic pain and infertility, characterized by the growth of ectopic endometrium outside the uterus. Given its still unknown etiology, treatments usually aim at diminishing pain and/or achieving pregnancy. Despite some progress in defining mode-of-action for drug development, the lack of reliable animal models indicates that novel approaches are required. The difficulties inherent to modeling endometriosis are related to its multifactorial nature, a condition that hinders the recreation of its pathology and the identification of clinically relevant metrics to assess drug efficacy. In this review, we report and comment endometriosis models and how they have led to new therapies. We envision a roadmap for endometriosis research, integrating Artificial Intelligence, three-dimensional cultures and organ-on-chip models as ways to achieve better understanding of physiopathological features and better tailored effective treatments. |
format | Online Article Text |
id | pubmed-7425005 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74250052020-08-16 Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology Malvezzi, Helena Marengo, Eliana Blini Podgaec, Sérgio Piccinato, Carla de Azevedo J Transl Med Review Endometriosis is a chronic inflammatory hormone-dependent condition associated with pelvic pain and infertility, characterized by the growth of ectopic endometrium outside the uterus. Given its still unknown etiology, treatments usually aim at diminishing pain and/or achieving pregnancy. Despite some progress in defining mode-of-action for drug development, the lack of reliable animal models indicates that novel approaches are required. The difficulties inherent to modeling endometriosis are related to its multifactorial nature, a condition that hinders the recreation of its pathology and the identification of clinically relevant metrics to assess drug efficacy. In this review, we report and comment endometriosis models and how they have led to new therapies. We envision a roadmap for endometriosis research, integrating Artificial Intelligence, three-dimensional cultures and organ-on-chip models as ways to achieve better understanding of physiopathological features and better tailored effective treatments. BioMed Central 2020-08-12 /pmc/articles/PMC7425005/ /pubmed/32787880 http://dx.doi.org/10.1186/s12967-020-02471-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Review Malvezzi, Helena Marengo, Eliana Blini Podgaec, Sérgio Piccinato, Carla de Azevedo Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology |
title | Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology |
title_full | Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology |
title_fullStr | Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology |
title_full_unstemmed | Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology |
title_short | Endometriosis: current challenges in modeling a multifactorial disease of unknown etiology |
title_sort | endometriosis: current challenges in modeling a multifactorial disease of unknown etiology |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425005/ https://www.ncbi.nlm.nih.gov/pubmed/32787880 http://dx.doi.org/10.1186/s12967-020-02471-0 |
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