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      Capillary recruitment in a theoretical model for blood flow regulation in heterogeneous microvessel networks

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          Abstract

          In striated muscle, the number of capillaries containing moving red blood cells increases with increasing metabolic demand. This phenomenon, termed capillary recruitment, has long been recognized, but its mechanism has been unclear. Here, a theoretical model for metabolic blood flow regulation in a heterogeneous network is used to test the hypothesis that capillary recruitment occurs as a result of active control of arteriolar diameters, combined with unequal partition of hematocrit at diverging microvascular bifurcations. The network structure is derived from published observations of hamster cremaster muscle in control and dilated states. The model for modulation of arteriolar diameters includes length-tension characteristics of vascular smooth muscle and responses of smooth muscle tone to myogenic, shear-dependent, and metabolic stimuli. Blood flow is simulated including nonuniform hematocrit distribution. Convective and diffusive oxygen transport in the network is simulated. Oxygen-dependent metabolic signals are assumed to be conducted upstream from distal vessels to arterioles. With increasing oxygen demand, arterioles dilate, blood flow increases, and the numbers of flowing arterioles and capillaries, as defined by red blood cell flux above a small threshold value, increase. Unequal hematocrit partition at diverging bifurcations contributes to recruitment and enhances tissue oxygenation. The results imply that capillary recruitment, as observed in the hamster cremaster preparations, can occur as a consequence of local control of arteriolar tone and the resulting nonuniform changes in red blood cell fluxes, and provide an explanation for observations of sequential recruitment of individual capillaries in response to modulation of terminal arteriolar diameter.

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          The number and distribution of capillaries in muscles with calculations of the oxygen pressure head necessary for supplying the tissue.

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            Biophysical aspects of blood flow in the microvasculature.

            The main function of the microvasculature is transport of materials. Water and solutes are carried by blood through the microvessels and exchanged, through vessel walls, with the surrounding tissues. This transport function is highly dependent on the architecture of the microvasculature and on the biophysical behavior of blood flowing through it. For example, the hydrodynamic resistance of a microvascular network, which determines the overall blood flow for a given perfusion pressure, depends on the number, size and arrangement of microvessels, the passive and active mechanisms governing their diameters, and on the apparent viscosity of blood flowing in them. Suspended elements in blood, especially red blood cells, strongly influence the apparent viscosity, which varies with several factors, including vessel diameter, hematocrit and blood flow velocity. The distribution of blood flows and red cell fluxes within a network, which influences the spatial pattern of mass transport, is determined by the mechanics of red cell motion in individual diverging bifurcations. Here, our current understanding of the biophysical processes governing blood flow in the microvasculature is reviewed, and some directions for future research are indicated.
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              Skeletal muscle blood flow in humans and its regulation during exercise.

              Regional limb blood flow has been measured with dilution techniques (cardio-green or thermodilution) and ultrasound Doppler. When applied to the femoral artery and vein at rest and during dynamical exercise these methods give similar reproducible results. The blood flow in the femoral artery is approximately 0.3 L min(-1) at rest and increases linearly with dynamical knee-extensor exercise as a function of the power output to 6-10 L min[-1] (Q= 1.94 + 0.07 load). Considering the size of the knee-extensor muscles, perfusion during peak effort may amount to 2-3 L kg(-1) min(-1), i.e. approximately 100-fold elevation from rest. The onset of hyperaemia is very fast at the start of exercise with T 1/2 of 2-10 s related to the power output with the muscle pump bringing about the very first increase in blood flow. A steady level is reached within approximately 10-150 s of exercise. At all exercise intensities the blood flow fluctuates primarily due to the variation in intramuscular pressure, resulting in a phase shift with the pulse pressure as a superimposed minor influence. Among the many vasoactive compounds likely to contribute to the vasodilation after the first contraction adenosine is a primary candidate as it can be demonstrated to (1) cause a change in limb blood flow when infused i.a., that is similar in time and magnitude as observed in exercise, and (2) become elevated in the interstitial space (microdialysis technique) during exercise to levels inducing vasodilation. NO appears less likely since NOS blockade with L-NMMA causing a reduced blood flow at rest and during recovery, it has no effect during exercise. Muscle contraction causes with some delay (60 s) an elevation in muscle sympathetic nerve activity (MSNA), related to the exercise intensity. The compounds produced in the contracting muscle activating the group IIl-IV sensory nerves (the muscle reflex) are unknown. In small muscle group exercise an elevation in MSNA may not cause vasoconstriction (functional sympatholysis). The mechanism for functional sympatholysis is still unknown. However, when engaging a large fraction of the muscle mass more intensely during exercise, the MSNA has an important functional role in maintaining blood pressure by limiting blood flow also to exercising muscles.
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                Author and article information

                Journal
                Physiol Rep
                Physiol Rep
                phy2
                Physiological Reports
                Blackwell Publishing Ltd
                2051-817X
                2051-817X
                August 2013
                22 August 2013
                : 1
                : 3
                : e00050
                Affiliations
                [1 ]Program in Applied Mathematics, University of Arizona Tucson, Arizona, 85721
                [2 ]Department of Anesthesiology, Mayo Clinic Rochester, Minnesota, 55905
                [3 ]Department of Physiology, University of Arizona Tucson, Arizona, 85724
                Author notes
                Timothy W. Secomb, Department of Physiology, University of Arizona, Box 245051, Tucson, Arizona 85724.Tel: +520-626-4513 Fax: +520-626-3376 E-mail: secomb@ 123456u.arizona.edu

                Funding Information Supported by National Institutes of Health grant HL070657.

                Article
                10.1002/phy2.50
                3770315
                24040516
                cc6e3a4e-aa1b-4362-ae81-aec6087e95a6
                © 2013 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of the American Physiological Society and The Physiological Society

                Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

                History
                : 14 June 2013
                : 10 July 2013
                : 10 July 2013
                Categories
                Original Research

                blood flow,capillary,computer modeling
                blood flow, capillary, computer modeling

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