从时间杀伤曲线了解细菌抗菌异质性的 "出生-死亡模型"。

IF 1.9 4区 数学 Q2 BIOLOGY Mathematical Biosciences Pub Date : 2024-08-23 DOI:10.1016/j.mbs.2024.109278
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引用次数: 0

摘要

抗菌药异抗性是指在同一细菌分离物中存在不同的亚群,它们对抗菌药的反应各不相同,因此与主群相比,它们表现出较低的敏感性。尽管人们普遍认为异抗性对抗菌治疗的结果起着至关重要的作用,但考虑到细菌异抗性的预测性抗菌药耐药性(AMR)模型仍然很少,需要随着异抗性测量技术的标准化和从数据中得出一致的结论而不断完善。在这项工作中,我们提出了细菌异抗性的多变量出生-死亡(BD)模型,并详细分析了其特性。我们考虑了种群动态中的随机性,因为异抗性的特点通常是低易感亚群的初始频率较低,这些亚群介导着 AMR 的传播,并可能导致治疗失败。我们还讨论了异抗性模型在实际应用中的实用性以及在现实条件下的校准问题,证明可以通过时杀数据推断模型参数和异抗性分布,即只测量细胞总数,而不进行任何异抗性测试。
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A birth–death model to understand bacterial antimicrobial heteroresistance from time-kill curves

Antimicrobial heteroresistance refers to the presence of different subpopulations with heterogeneous antimicrobial responses within the same bacterial isolate, so they show reduced susceptibility compared with the main population. Though it is widely accepted that heteroresistance can play a crucial role in the outcome of antimicrobial treatments, predictive Antimicrobial Resistance (AMR) models accounting for bacterial heteroresistance are still scarce and need to be refined as the techniques to measure heteroresistance become standardised and consistent conclusions are drawn from data. In this work, we propose a multivariate Birth-Death (BD) model of bacterial heteroresistance and analyse its properties in detail. Stochasticity in the population dynamics is considered since heteroresistance is often characterised by low initial frequencies of the less susceptible subpopulations, those mediating AMR transmission and potentially leading to treatment failure. We also discuss the utility of the heteroresistance model for practical applications and calibration under realistic conditions, demonstrating that it is possible to infer the model parameters and heteroresistance distribution from time-kill data, i.e., by measuring total cell counts alone and without performing any heteroresistance test.

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来源期刊
Mathematical Biosciences
Mathematical Biosciences 生物-生物学
CiteScore
7.50
自引率
2.30%
发文量
67
审稿时长
18 days
期刊介绍: Mathematical Biosciences publishes work providing new concepts or new understanding of biological systems using mathematical models, or methodological articles likely to find application to multiple biological systems. Papers are expected to present a major research finding of broad significance for the biological sciences, or mathematical biology. Mathematical Biosciences welcomes original research articles, letters, reviews and perspectives.
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