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算法、建模和 VO₂ 动力学。

Algorithms, modelling and VO₂ kinetics.

机构信息

Department of Visual and Neurological Sciences, School of Exercise and Sports Sciences, University of Verona, Via Felice Casorati 43, 37131, Verona, Italy.

出版信息

Eur J Appl Physiol. 2011 Mar;111(3):331-42. doi: 10.1007/s00421-010-1396-8. Epub 2010 Feb 27.

Abstract

This article summarises the pros and cons of different algorithms developed for estimating breath-by-breath (B-by-B) alveolar O(2) transfer (VO 2A) in humans. VO 2A is the difference between O(2) uptake at the mouth and changes in alveolar O(2) stores (∆ VO(2s)), which for any given breath, are equal to the alveolar volume change at constant FAO2/FAiO2 ∆VAi plus the O(2) alveolar fraction change at constant volume [V Ai-1(F Ai - F Ai-1) O2, where V (Ai-1) is the alveolar volume at the beginning of a breath. Therefore, VO 2A can be determined B-by-B provided that V (Ai-1) is: (a) set equal to the subject's functional residual capacity (algorithm of Auchincloss, A) or to zero; (b) measured (optoelectronic plethysmography, OEP); (c) selected according to a procedure that minimises B-by-B variability (algorithm of Busso and Robbins, BR). Alternatively, the respiratory cycle can be redefined as the time between equal FO(2) in two subsequent breaths (algorithm of Grønlund, G), making any assumption of V (Ai-1) unnecessary. All the above methods allow an unbiased estimate of VO2 at steady state, albeit with different precision. Yet the algorithms "per se" affect the parameters describing the B-by-B kinetics during exercise transitions. Among these approaches, BR and G, by increasing the signal-to-noise ratio of the measurements, reduce the number of exercise repetitions necessary to study VO2 kinetics, compared to A approach. OEP and G (though technically challenging and conceptually still debated), thanks to their ability to track ∆VO(2s) changes during the early phase of exercise transitions, appear rather promising for investigating B-by-B gas exchange.

摘要

本文总结了不同算法在估算人体逐口气(B-by-B)肺泡 O2 转运(VO2A)方面的优缺点。VO2A 是口腔 O2 摄取量与肺泡 O2 储存量变化之差(∆VO2s),对于任何给定的呼吸,都等于在恒定 FAO2/FAiO2 下的肺泡体积变化(∆VAi)加上在恒定体积下的 O2 肺泡分数变化[V Ai-1(FAi-F Ai-1)O2,其中 V(Ai-1)是呼吸开始时的肺泡体积。因此,只要 V(Ai-1)为:(a)等于受检者的功能残气量(Auchincloss 算法,A)或为零;(b)测量(光电体积描记法,OEP);(c)根据最小化 B-by-B 变异性的程序选择(Busso 和 Robbins 算法,BR),就可以逐口气确定 VO2A。或者,可以将呼吸周期重新定义为随后两次呼吸中相等 FO2 之间的时间(Grønlund 算法,G),从而无需对 V(Ai-1)做出任何假设。所有上述方法都可以在稳定状态下对 VO2 进行无偏估计,尽管精度不同。然而,这些算法“本身”会影响描述运动过渡期间 B-by-B 动力学的参数。在这些方法中,BR 和 G 通过增加测量的信噪比,与 A 方法相比,减少了研究 VO2 动力学所需的运动重复次数。OEP 和 G(尽管在技术上具有挑战性且在概念上仍存在争议),由于它们能够在运动过渡的早期阶段跟踪 ∆VO2s 变化,因此对于研究 B-by-B 气体交换似乎很有前途。

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