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The carotid body is an arterial chemoreceptor organ that senses arterial pO(2) and pH. Previous studies have indicated that both reactive oxygen species (ROS) and nitric oxide (NO) are important potential mediators that may be involved in the response of the carotid body to hypoxia. However, whether their production by the chemosensitive elements of the carotid body is indeed oxygen-dependent is currently unclear. Thus, we have investigated their production under normoxic (20% O(2)) and hypoxic (1% O(2)) conditions in slice preparations of the rat carotid body by using fluorescent indicators and confocal microscopy. NO-synthesizing enzymes were identified by immunohistochemistry and histochemistry, and the subcellular localization of the NO-sensitive indicator diaminofluorescein was determined by a photoconversion technique and electron microscopy. Glomus cells of the carotid body responded to hypoxia by increases in both ROS and NO production. The hypoxia-induced increase in NO generation required (to a large extent, but not completely) extracellular calcium. Glomus cells were immunoreactive to endothelial NO synthase but not to the neuronal or inducible isoforms. Ultrastructurally, the NO-sensitive indicator was observed in mitochondrial membranes after exposure to hypoxia. The data show that glomus cells respond to exposure to hypoxia by the enhanced production of both ROS and NO. NO production by glomus cells is probably mediated by endothelial NO synthase, which is activated by calcium influx. The presence of NO indicator in mitochondria suggests the hypoxic regulation of mitochondrial function via NO in glomus cells.

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Modern imaging techniques can provide sequences of images giving signals proportional to the concentrations of tracers (by emission tomography), of X-ray-absorbing contrast materials (fast CT or perhaps NMR contrast), or of native chemical substances (NMR) in tissue regions at identifiable locations in 3D space. Methods for the analysis of the concentration-time curves with mathematical models describing the physiological processes and the appropriate anatomy are now available to give a quantitative portrayal of both structure and function: such is the approach to metabolic or functional imaging. One formulates a model first by defining what it should represent: this is the hypothesis. When translated into a self-consistent set of differential equations, the model becomes a mathematical model, a quantitative version of the hypothesis. This is what one would like to test against data. However, the next step is to reduce the mathematical model to a computable form; anatomically and physiologically realistic models account of the spatial gradients in concentrations within blood-tissue exchange units, while compartmental models simplify the equations by using the average concentrations. The former are known as distributed models and the latter as lumped compartmental or mixing chamber models. Since both are derived from the same ideas, the parameters are usually the same; their differences are in their ability to represent the hypothesis correctly, quantitatively, and sometimes in their computability. In this essay we review the philosophical and practical aspects of such modelling analysis for translating image sequences into physiological terms.