Mathematical modeling of ventilator-induced lung inflammation
Respiratory infections, such as the novel coronavirus (SARS-COV-2) and other lung injuries, damage the pulmonary epithelium. In the most severe cases this leads to acute respiratory distress syndrome (ARDS). Due to respiratory failure associated with ARDS, the clinical intervention is the use of mechanical ventilation. Despite the benefits of mechanical ventilators, prolonged or misuse of these ventilators may lead to ventilation-associated/ventilation-induced lung injury (VILI). Damage caused to epithelial cells within the alveoli can lead to various types of complications and increased mortality rates. A key component of the immune response is recruitment of macrophages, immune cells that differentiate into phenotypes with unique pro- and/or anti-inflammatory roles based on the surrounding environment. An imbalance in pro- and anti-inflammatory responses can have deleterious effects on the individual's health. To gain a greater understanding of the mechanisms of the immune response to VILI and post-ventilation outcomes, we develop a mathematical model of interactions between the immune system and site of damage while accounting for macrophage polarization. Through Latin hypercube sampling we generate a virtual cohort of patients with biologically feasible dynamics. We use a variety of methods to analyze the results, including a random forest decision tree algorithm and parameter sensitivity with eFAST. Analysis shows that parameters and properties of transients related to epithelial repair and M1 activation and de-activation best predicted outcome. Using this new information, we hypothesize inter-ventions and use these treatment strategies to modulate damage in select virtual cases.
| Errataetall: |
UpdateIn: J Theor Biol. 2021 Apr 27;:110738. - PMID 33930440 |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2020 |
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| Erschienen: |
2020 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2020 |
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| Enthalten in: |
bioRxiv : the preprint server for biology - (2020) vom: 17. Nov. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Minucci, Sarah [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 11.06.2021 published: Electronic UpdateIn: J Theor Biol. 2021 Apr 27;:110738. - PMID 33930440 Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1101/2020.06.03.132258 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM318027178 |
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| 500 | |a UpdateIn: J Theor Biol. 2021 Apr 27;:110738. - PMID 33930440 | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Respiratory infections, such as the novel coronavirus (SARS-COV-2) and other lung injuries, damage the pulmonary epithelium. In the most severe cases this leads to acute respiratory distress syndrome (ARDS). Due to respiratory failure associated with ARDS, the clinical intervention is the use of mechanical ventilation. Despite the benefits of mechanical ventilators, prolonged or misuse of these ventilators may lead to ventilation-associated/ventilation-induced lung injury (VILI). Damage caused to epithelial cells within the alveoli can lead to various types of complications and increased mortality rates. A key component of the immune response is recruitment of macrophages, immune cells that differentiate into phenotypes with unique pro- and/or anti-inflammatory roles based on the surrounding environment. An imbalance in pro- and anti-inflammatory responses can have deleterious effects on the individual's health. To gain a greater understanding of the mechanisms of the immune response to VILI and post-ventilation outcomes, we develop a mathematical model of interactions between the immune system and site of damage while accounting for macrophage polarization. Through Latin hypercube sampling we generate a virtual cohort of patients with biologically feasible dynamics. We use a variety of methods to analyze the results, including a random forest decision tree algorithm and parameter sensitivity with eFAST. Analysis shows that parameters and properties of transients related to epithelial repair and M1 activation and de-activation best predicted outcome. Using this new information, we hypothesize inter-ventions and use these treatment strategies to modulate damage in select virtual cases | ||
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| 700 | 1 | |a Heise, Rebecca L |e verfasserin |4 aut | |
| 700 | 1 | |a Valentine, Michael S |e verfasserin |4 aut | |
| 700 | 1 | |a Kamga Gninzeko, Franck J |e verfasserin |4 aut | |
| 700 | 1 | |a Reynolds, Angela M |e verfasserin |4 aut | |
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