Computational Methodologies for the
citrullinated histone
machine-learning
myeloperoxidase
neutrophil elastase
neutrophil extracellular traps
Journal
Frontiers in immunology
ISSN: 1664-3224
Titre abrégé: Front Immunol
Pays: Switzerland
ID NLM: 101560960
Informations de publication
Date de publication:
2019
2019
Historique:
received:
14
12
2018
accepted:
24
06
2019
entrez:
30
7
2019
pubmed:
30
7
2019
medline:
5
6
2020
Statut:
epublish
Résumé
Neutrophil extracellular traps (NETs) are a neutrophil defensive mechanism where chromatin is expelled together with antimicrobial proteins in response to a number of stimuli. Even though beneficial in many cases, their dysfunction has been implicated in many diseases, such as rheumatoid arthritis and cancer. Accurate quantification of NETs is of utmost importance for correctly studying their role in various diseases, especially when considering them as therapeutic targets. Unfortunately, NET quantification has a number of limitations. However, recent developments in computational methodologies for quantifying NETs have vastly improved the ability to study NETs. Methods range from using ImageJ to user friendly applications and to more sophisticated machine-learning approaches. These various methods are reviewed and discussed in this review.
Identifiants
pubmed: 31354718
doi: 10.3389/fimmu.2019.01562
pmc: PMC6635468
doi:
Substances chimiques
Peroxidase
EC 1.11.1.7
Leukocyte Elastase
EC 3.4.21.37
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
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