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Modeling the effect of oxygen on the amperometric response of immobilized organoselenium-based S-nitrosothiol sensors.

ABSTRACT: Amperometric detection of S-nitrosothiols (RSNOs) at submicromolar levels in blood samples is of potential importance for monitoring endothelial function and other disease states that involve changes in physiological nitric oxide (NO) production. It is shown here that the elimination of dissolved oxygen from samples is critical when covalently attached diselenocystamine-based amperometric RSNO sensors are used for practical RSNO measurements. The newest generation of RSNO sensors utilizes an amperometric NO gas sensor with a thin organoselenium modified dialysis membrane mounted at the distal sensing tip. Sample RSNOs are catalytically reduced to NO within the dialysis membrane by the immobilized organoselenium species. In the presence of oxygen, the sensitivity of these sensors for measuring low levels of RSNOs (

SUBMITTER: Hofler L

PROVIDER: S-EPMC3033888 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature

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Modeling the effect of oxygen on the amperometric response of immobilized organoselenium-based S-nitrosothiol sensors.

Höfler Lajos L Meyerhoff Mark E ME

Analytical chemistry 20110101 2

Amperometric detection of S-nitrosothiols (RSNOs) at submicromolar levels in blood samples is of potential importance for monitoring endothelial function and other disease states that involve changes in physiological nitric oxide (NO) production. It is shown here that the elimination of dissolved oxygen from samples is critical when covalently attached diselenocystamine-based amperometric RSNO sensors are used for practical RSNO measurements. The newest generation of RSNO sensors utilizes an amp ...[more]

PMID: 21230000

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Project description:Oxygen consumption ([Formula: see text]) provides established clinical and physiological indicators of cardiorespiratory function and exercise capacity. However, [Formula: see text] monitoring is largely limited to specialized laboratory settings, making its widespread monitoring elusive. Here we investigate temporal prediction of [Formula: see text] from wearable sensors during cycle ergometer exercise using a temporal convolutional network (TCN). Cardiorespiratory signals were acquired from a smart shirt with integrated textile sensors alongside ground-truth [Formula: see text] from a metabolic system on 22 young healthy adults. Participants performed one ramp-incremental and three pseudorandom binary sequence exercise protocols to assess a range of [Formula: see text] dynamics. A TCN model was developed using causal convolutions across an effective history length to model the time-dependent nature of [Formula: see text]. Optimal history length was determined through minimum validation loss across hyperparameter values. The best performing model encoded 218 s history length (TCN-VO2 A), with 187, 97, and 76 s yielding <3% deviation from the optimal validation loss. TCN-VO2 A showed strong prediction accuracy (mean, 95% CI) across all exercise intensities (-22 ml min-1, [-262, 218]), spanning transitions from low-moderate (-23 ml min-1, [-250, 204]), low-high (14 ml min-1, [-252, 280]), ventilatory threshold-high (-49 ml min-1, [-274, 176]), and maximal (-32 ml min-1, [-261, 197]) exercise. Second-by-second classification of physical activity across 16,090 s of predicted [Formula: see text] was able to discern between vigorous, moderate, and light activity with high accuracy (94.1%). This system enables quantitative aerobic activity monitoring in non-laboratory settings, when combined with tidal volume and heart rate reserve calibration, across a range of exercise intensities using wearable sensors for monitoring exercise prescription adherence and personal fitness.

Dataset Information

Modeling the effect of oxygen on the amperometric response of immobilized organoselenium-based S-nitrosothiol sensors.

Publications

Modeling the effect of oxygen on the amperometric response of immobilized organoselenium-based S-nitrosothiol sensors.

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