COVID-19 and silent hypoxemia in a minimal closed-loop model of the respiratory rhythm generator.

IF 1.7 4区 工程技术 Q3 COMPUTER SCIENCE, CYBERNETICS
Biological Cybernetics Pub Date : 2024-08-01 Epub Date: 2024-06-17 DOI:10.1007/s00422-024-00989-w
Casey O Diekman, Peter J Thomas, Christopher G Wilson
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引用次数: 0

Abstract

Silent hypoxemia, or "happy hypoxia," is a puzzling phenomenon in which patients who have contracted COVID-19 exhibit very low oxygen saturation ( SaO 2 < 80%) but do not experience discomfort in breathing. The mechanism by which this blunted response to hypoxia occurs is unknown. We have previously shown that a computational model of the respiratory neural network (Diekman et al. in J Neurophysiol 118(4):2194-2215, 2017) can be used to test hypotheses focused on changes in chemosensory inputs to the central pattern generator (CPG). We hypothesize that altered chemosensory function at the level of the carotid bodies and/or the nucleus tractus solitarii are responsible for the blunted response to hypoxia. Here, we use our model to explore this hypothesis by altering the properties of the gain function representing oxygen sensing inputs to the CPG. We then vary other parameters in the model and show that oxygen carrying capacity is the most salient factor for producing silent hypoxemia. We call for clinicians to measure hematocrit as a clinical index of altered physiology in response to COVID-19 infection.

Abstract Image

呼吸节律发生器最小闭环模型中的 COVID-19 和无声低氧血症。
无声低氧血症或 "快乐低氧 "是一种令人费解的现象,感染 COVID-19 的患者会表现出极低的血氧饱和度(SaO 2 < 80%),但不会感到呼吸不适。这种对缺氧反应迟钝的机制尚不清楚。我们之前已经证明,呼吸神经网络的计算模型(Diekman 等人,载于 J Neurophysiol 118(4):2194-2215,2017 年)可用于测试以中央模式发生器(CPG)化学感觉输入变化为重点的假设。我们假设,颈动脉体和/或脊髓束核水平的化学感觉功能改变是导致对缺氧反应迟钝的原因。在这里,我们利用我们的模型,通过改变代表氧传感输入到 CPG 的增益函数的特性来探索这一假设。然后,我们改变了模型中的其他参数,结果表明携氧能力是产生无声低氧血症的最突出因素。我们呼吁临床医生测量血细胞比容,将其作为 COVID-19 感染后生理变化的临床指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biological Cybernetics
Biological Cybernetics 工程技术-计算机:控制论
CiteScore
3.50
自引率
5.30%
发文量
38
审稿时长
6-12 weeks
期刊介绍: Biological Cybernetics is an interdisciplinary medium for theoretical and application-oriented aspects of information processing in organisms, including sensory, motor, cognitive, and ecological phenomena. Topics covered include: mathematical modeling of biological systems; computational, theoretical or engineering studies with relevance for understanding biological information processing; and artificial implementation of biological information processing and self-organizing principles. Under the main aspects of performance and function of systems, emphasis is laid on communication between life sciences and technical/theoretical disciplines.
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