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Computational Models for Diagnosing and Treating Endometriosis

Endometriosis is a common but poorly understood disease. Symptoms can begin early in adolescence, with menarche, and can be debilitating. Despite this, people often suffer several years before being correctly diagnosed and adequately treated. Endometriosis involves the inappropriate growth of endome...

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Autores principales: Mbuguiro, Wangui, Gonzalez, Adriana Noemi, Mac Gabhann, Feilim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580807/
https://www.ncbi.nlm.nih.gov/pubmed/36303959
http://dx.doi.org/10.3389/frph.2021.699133
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author Mbuguiro, Wangui
Gonzalez, Adriana Noemi
Mac Gabhann, Feilim
author_facet Mbuguiro, Wangui
Gonzalez, Adriana Noemi
Mac Gabhann, Feilim
author_sort Mbuguiro, Wangui
collection PubMed
description Endometriosis is a common but poorly understood disease. Symptoms can begin early in adolescence, with menarche, and can be debilitating. Despite this, people often suffer several years before being correctly diagnosed and adequately treated. Endometriosis involves the inappropriate growth of endometrial-like tissue (including epithelial cells, stromal fibroblasts, vascular cells, and immune cells) outside of the uterus. Computational models can aid in understanding the mechanisms by which immune, hormone, and vascular disruptions manifest in endometriosis and complicate treatment. In this review, we illustrate how three computational modeling approaches (regression, pharmacokinetics/pharmacodynamics, and quantitative systems pharmacology) have been used to improve the diagnosis and treatment of endometriosis. As we explore these approaches and their differing detail of biological mechanisms, we consider how each approach can answer different questions about endometriosis. We summarize the mathematics involved, and we use published examples of each approach to compare how researchers: (1) shape the scope of each model, (2) incorporate experimental and clinical data, and (3) generate clinically useful predictions and insight. Lastly, we discuss the benefits and limitations of each modeling approach and how we can combine these approaches to further understand, diagnose, and treat endometriosis.
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spelling pubmed-95808072022-10-26 Computational Models for Diagnosing and Treating Endometriosis Mbuguiro, Wangui Gonzalez, Adriana Noemi Mac Gabhann, Feilim Front Reprod Health Reproductive Health Endometriosis is a common but poorly understood disease. Symptoms can begin early in adolescence, with menarche, and can be debilitating. Despite this, people often suffer several years before being correctly diagnosed and adequately treated. Endometriosis involves the inappropriate growth of endometrial-like tissue (including epithelial cells, stromal fibroblasts, vascular cells, and immune cells) outside of the uterus. Computational models can aid in understanding the mechanisms by which immune, hormone, and vascular disruptions manifest in endometriosis and complicate treatment. In this review, we illustrate how three computational modeling approaches (regression, pharmacokinetics/pharmacodynamics, and quantitative systems pharmacology) have been used to improve the diagnosis and treatment of endometriosis. As we explore these approaches and their differing detail of biological mechanisms, we consider how each approach can answer different questions about endometriosis. We summarize the mathematics involved, and we use published examples of each approach to compare how researchers: (1) shape the scope of each model, (2) incorporate experimental and clinical data, and (3) generate clinically useful predictions and insight. Lastly, we discuss the benefits and limitations of each modeling approach and how we can combine these approaches to further understand, diagnose, and treat endometriosis. Frontiers Media S.A. 2021-12-20 /pmc/articles/PMC9580807/ /pubmed/36303959 http://dx.doi.org/10.3389/frph.2021.699133 Text en Copyright © 2021 Mbuguiro, Gonzalez and Mac Gabhann. 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 Reproductive Health
Mbuguiro, Wangui
Gonzalez, Adriana Noemi
Mac Gabhann, Feilim
Computational Models for Diagnosing and Treating Endometriosis
title Computational Models for Diagnosing and Treating Endometriosis
title_full Computational Models for Diagnosing and Treating Endometriosis
title_fullStr Computational Models for Diagnosing and Treating Endometriosis
title_full_unstemmed Computational Models for Diagnosing and Treating Endometriosis
title_short Computational Models for Diagnosing and Treating Endometriosis
title_sort computational models for diagnosing and treating endometriosis
topic Reproductive Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9580807/
https://www.ncbi.nlm.nih.gov/pubmed/36303959
http://dx.doi.org/10.3389/frph.2021.699133
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