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Quantifying relative virulence: when μ (max) fails and AUC alone just is not enough
A challenge in virology is quantifying relative virulence (V (R)) between two (or more) viruses that exhibit different replication dynamics in a given susceptible host. Host growth curve analysis is often used to mathematically characterize virus–host interactions and to quantify the magnitude of de...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Microbiology Society
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8116781/ https://www.ncbi.nlm.nih.gov/pubmed/33151141 http://dx.doi.org/10.1099/jgv.0.001515 |
Sumario: | A challenge in virology is quantifying relative virulence (V (R)) between two (or more) viruses that exhibit different replication dynamics in a given susceptible host. Host growth curve analysis is often used to mathematically characterize virus–host interactions and to quantify the magnitude of detriment to host due to viral infection. Quantifying V (R) using canonical parameters, like maximum specific growth rate (μ (max)), can fail to provide reliable information regarding virulence. Although area-under-the-curve (AUC) calculations are more robust, they are sensitive to limit selection. Using empirical data from Sulfolobus Spindle-shaped Virus (SSV) infections, we introduce a novel, simple metric that has proven to be more robust than existing methods for assessing V (R). This metric (I (SC)) accurately aligns biological phenomena with quantified metrics to determine V (R). It also addresses a gap in virology by permitting comparisons between different non-lytic virus infections or non-lytic versus lytic virus infections on a given host in single-virus/single-host infections. |
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