Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_3787_Библиотеки_им_академика_М_И_Перельмана
.pdf
Appendix 4: Coronary Flow Regulation 435
https://t.me/med1917
in the coronary venous ATP concentration, and the latter correlates with the decline
of venous PO
vessel metabolic signal F
(Farias III et al., 2005). In the network flow analysis, the terminal
2
i
is either set to be constant in all terminal vessels or
, j
mterm
optimized in each terminal vessel to provide a set level of terminal flow.
Oxygen Deman d and Target Terminal Flow If the secondary contribution of
dissolved oxygen on the total oxygen content are considered negligible, the oxygen
mass balance is specified by:
M ¼ q
where M is myocardial oxygen consumption for a single terminal arteriole, q
the flow in the terminal arterioles, H
saturation, S
is the venous oxygen saturation, and cois the oxygen carrying capacity
v
c0HDSa S
ðÞ ð6:100Þ
term
is the hematocrit, Sais arterial oxygen
D
v
term
of RBCs. Hence, the following holds:
M
c
0HDSa
S
ðÞ
v
The values of c
¼
q
term
, and Saare directly measurable and assumed to be constant
0,HD
(independent of M ). The venous oxygen saturation S
is a function of the oxygen
v
ð6:101Þ
consumption M, being dependent on oxygen mass balance, ATP release and transport, and the effect of sympathetic inputs on myocardial oxygen consum ption. It is
thus affected by the combined action of a feedback pathway signal that is determined
by the level of plasma ATP in coronary venous blood, and by adrenergic open-loop
(feedforward) signal that increases with exercise (Pradhan, Feigl, Gorman,
Brengelmann, & Beard, 2016). Data has been measured by (Farias III et al., 2005)
and (Gorman et al., 2010; Gorman, Tune, Richmond, & Feigl, 2000). Based on this
relationship between M and S
, the flow in terminal vessels q
v
can be directly
term
related to the oxygen consumption M.
Flow Regulation Time Constant The time constant of the coronary vessel
response to changes in pressure and flow (approximately 1.5 folds of t
—the time
50
required to establish half of the complete response) is found to be in the range of 15 s
to minutes (Dankelman et al., 1992; Hoffman & Spaan, 1990; Mosher, Ross Jr.,
McFate, & Shaw, 1964; Tsoukias, Kavdia, & Popel, 2004). This response time
constant is significantly higher than the cardiac period (~1 s). The stabilized system
response can thus be considered as the time average over a cardiac cycle. Hence, the
levels of regulated vessel radius (Eq. 6.89), of the active tensi on (Eq. 6.92) and of the
active stiffness (Eq. 6.86) are formulated as functions of the time-averaged transvascular pressure.
is
Boundary Conditions The coronary flow is determined by the myocardium–vessel
interaction (MVI) which consists of the combined effect of the intramyocardial fluid
pressure (IMP) and the shortening-induced intramyocyte pressure P
with the myocardial relative depth (MRD) from the LV pressure at the endocardium
. IMP varies
SIP
436 6 Network Analysis of Coronary Circulation: II. Pulsatile Flow
https://t.me/med1917
to zero at the epicardium. Waveforms of the inlet pressure, Pin(t), outlet pressure,
P
(t), LV pressure, PLV(t), and intramyocyte pressure, P
out
the flow analysis (Fig. A.5). The P
(t) signal is interpolated for different transmural
out
(t), are input signals to
SIP
locations based on predictions from simulation of the unregulated flow in an entire
coronary network which included arterial and venous trees and four identical
representative capillary networks, at relative myocardial depths (MRD) of 0.125,
0.375, 0.625, and 0.875 (Algranati et al., 2010). P
(t) waveform is taken from
LV
predictions based on a distributive LV mechanical model under resting heart rate
(Kiyooka et al., 2005) of 75 BPM. Several considerations guided the choice of the
P
(t) signa l for the sub-endocardial 400 vessel network. The first is the pressure drop
in
from the aorta to the trunk vessel (order 6) of the subtree. On the other hand, there is
a pressure increase due to the added intramyocyte pressure, P
during contraction (Rabbany et al., 1989). Finally, P
flow perfusion in the terminal order 1 vessels in the range of measured flow of
0.4–2.0 10
these considerations, P
3mm3
/s in systole and diastole (Tillmanns et al., 1974). Based on
(t) is chosen to be 122/90 mmHg (with averagePin¼100) in
in
(t) must provide for sufficient
in
systole/diastole and the signal shape is adopted from (Algrana ti et al., 2010). P
(t) which develops
SIP
out
assigned for each terminal vessel to be between the previously predicted
sub-epicardium and sub-endocardium signals P
subepi
out
depending on the transmural location of the vessel. In the absence of data on P
under higher metabolic demands (i.e., higher q
the same under changes of q
. The tissue pressure PT(t) is derived based on the
target
), the level for each vessel is kept
target
and P
subendo
out
(Fig. 6.23),
out
earlier analysis of unregulated coronary flow.
is
Time-Varying Vessel Radius For flow analysis, Eq. (6.84) allows the calculation
of the requisite vessel radius, R(t), along the cardiac cycle. In the embedded and
tethered microvessel, the radius variations are likely small enough to retain just the
first term in the Taylor series expansion of R(t). In this case, the following can be
obtained:
RtðÞ’R
reg
þ
dR
ΔPtðÞΔPðÞ ð6:102Þ
dΔP
where ΔP(t) is the time-varying trans-vascular pressure along the cardiac cycle.
Solution of Network Flow Due to vessel elasticity and interaction with surrounding myocardium (MVI), the flow equations are highly nonlinear. Hence, network
flow solution is an iterative solution of the system of ODEs (Eq. 6.62) subject to the
respective boundary conditions. The matrices A and B are modified after each
iteration which are continued to reach the desired convergence and periodicity
conditions to within specified tolerances.
The numerical framework is used to first solve the passive network flow, followed
by active regulation. Two different schemes are used to solve the autoregulated flow.
In cases where metabolic regulation is absent (i.e., only myogenic and/or flow
mechanisms are active—the solution for a passive vessel network is used as the
Appendix 4: Coronary Flow Regulation 437
https://t.me/med1917
Fig. 6.23 The assigned pressure boundary conditions. Pin, the input pressure to the order 6 trunk
subendo
vessel; P
of 0.875; P
of 0.125. Both P
(Algranati et al., 2010). PLV pressure in LV chamber; PSIP intramyocyte pressure caused by their
shortening. Reproduced from Namani et al. (2018) by permission
, the output pressure at the terminal order 1 vessels in a normalized myocardial depth
out
subepi
, the output pressure at the terminal order 1 vessels in a normalized myocardial depth
out
subendo
out
and P
subepi
are adapted from previous analysis of unregulated coronary flow
out
initial guess to adjust each vessel diameter following the respective model equations,
according to its predicted pressure and flow rate). When the metabolic regulation is
active, the solution from passive network flow is iterated under a genetic algorithm
search for the distribution across all the j terminal arterioles of the metabolic signals
i
F
(Eq. 6.99) which yield terminal flow rates close to q
, j
meta
, up to within
target
specified tolerance. The solution of a reference case is carried out with parameters
listed in Table 6.3. It served as a baseline for the sensitivity analysis to compare
predictions under a range of parameter levels.
Simulations are carried out to verify the flow periodicity condition, i.e., smoothness of the transition between nodal pressures from the end of one cardiac cycle to
the start of the next. The smoothness tolerance is set to 0.075 mmHg. The convergence of the computational results is estimated based on the network flow solution,
where the net inflow/outflow deviation at each time point (from the requisite zero
level) is calculated at each vessel mid-node and at each coronary bifurcation. This is
carried out for both the passive and regulated network flow under both steady as well
as dynamic flow conditions. Convergence is satisfied when all flow deviations are
<1% of the inflow to the respective node and vessel junctions.
Model Comparison with Flow Characteristics
Since detailed quantitative validation data are presently not available for individual
vessels in vivo, model predictions are compared with global response patterns. To
that end, four sets of simulations are performed under the following conditions:
438 6 Network Analysis of Coronary Circulation: II. Pulsatile Flow
https://t.me/med1917
(Case 1) Full myogenic activation; (Case 2) Myogenic and shear activation with no
metabolic activation; (Case 3) Myogenic, shear, and full metabolic activation of
terminal order 1 vessels; and (Case 4) Myogenic, shear, and optimized metabolic
activation in terminal order 1 vessels aimed to achieve target flow level s in the
terminal arterioles. Case 1 is an arrested heart with the sole effect of pressure on flow
control. The shear and metabolic signals are deactivated by setting all F
F
in Eq. (6.97) to zero. Case 2 is also an arrested heart with the effects of pressure
meta
and shear on flow control. The metabolic signal is deactivated by setting all F
and all
τ
meta
in
Eq. (6.97) to zero. Case 3 represents the full highest flow capability of the system in a
beating heart. In Case 4, the optimal metabolic signals, F
1 vessels are targeted to obtain the least deviation of terminal perfusion level, q
from the set targe t flow level (q
target
).
i
of all terminal order
, j
mterm
j
term
The following model predictions are compared with observations: dispersion of
the perfusion, transmural perfusion heterogeneity, the magnitude of the metabolic
flow reserve (MFR), and autoregulatory response of the system flow.
Perfusion Dispersion Coronary perfusion in the passive (unregulated) state has
been shown to be highly heterogeneous (Austin Jr., Aldea, Coggins, Flynn, &
Hoffman, 1990; Huo et al., 2009). Regulation is found to decrease flow dispersion
(Austin Jr. et al., 1990). The predicted flow level and its dispersion (CV) are
evaluated here under various regulation mechanisms with full (F
(F
¼ 0) metabolic activation, and under optimized metabolic activation for
mterm
several levels of metabolic demand (i.e., target terminal vessel flow,q
mterm
¼ 1) and no
) and a
target
number of input perfusion pressures.
,
Transmural Perfusion Heterogeneity The effect of network transmural location
on perfusion and its dispersion is analyzed as (1) terminal arterioles flow q
term
under
various regulation mechanisms and reference perfusion pressure, and (2) optimized
flow under a few levels of metabolic demand and perfusion pressures. Transmural
heterogeneity is also studied in terms of the metabolic flow reserve (MFR, see
section below), the network total flow under different regulation mechanisms, and
autoregulation of the network flow.
Metabolic Flow Reserve (MFR) MFR is the flow reserve which results from flow
regulation alone, without the effects of heart rate and activation waveform. It is
estimated from the total flow in the active vessel network under myogenic, shear, and
metabolic activations. The minimum flow is found under myogenic and shear
activation and zero metabolic activation. The maximum flow is found under full
metabolic activation of all order 1 terminal vessels. MFR is estimated as a function
of the average input pressure (P
waveform and outlet pressure signal (P
) in the range of 45 to 180 mmHg under the same
in
).
out
Flow Autoregulation Autoregulation in coronary circulation under constant metabolic demand produces approximately constant network flow under an increase in
perfusion pressure. It is studied with the metabolic regulation optimized to provide
four q
levels of 1.20, 1.50, 1.80, and 2.25 103mm3/s. As one test of model
target
Appendix 6 439
https://t.me/med1917
validity, the subtree total flow Q is analyzed under changes in perfusion pressure for
four levels of requisite terminal flow q
also for the passive state, and under full (F
. For comparison, the flow is analyzed
target
¼ 1) and no (F
mterm
¼ 0) metabolic
mterm
activation.
Effect of Myocardial–Vessel Interaction (MVI) The vessel loading by the myocardium (MVI) has three passive components (Algranati et al., 2010; Young et al.,
2012): (1) chamber deriv ed intramyocardial tissue pressure; (2) intracellular pressure
in the contracting myocytes, and (3) effect of vessel tethering to the surrounding
myocardium. The effects of MVI on the flow are analyzed in terms of the MFR
(Namani et al., 2018), the significance of each regulation on the total network flow
and on flow autoregulation (e.g., effect on the distribution of metabolic activation for
various levels of metabolic demand and input perfusion pressure).
Order Dependence of the Metabolic Diameter Regulation The network vessels
increase their diameters in response to higher metabolic demand. The change in
diameters is, however, non-uniform, re flecting the likewise non-uniform effect of the
metabolic regulation on the different vessel orders.
Appendix 5
Effects of different regulations and of the transmural network location on the average
terminal arterioles flow rates under a reference average input perfusion pressure of
100 mmHg
Sub-endocardial Sub-epicardial
(103mm3/s) q
q
term
Flow control
Passive 2.78 0.69 0.25 3.34 0.81 0.24
Myogenic 0.29 0.10 0.35 0.51 0.23 0.46
Myogenic + Shear(all F
Myogenic + Shear + Full Metabolic Activation(all
F
¼ 1)
mterm
¼ 0) 0.65 0.19 0.29 0.91 0.31 0.34
mterm
Mean SD CV Mean SD CV
2.13 0.61 0.29 2.50 0.70 0.28
(103mm3/s)
term
Appendix 6
Effects of the target terminal flow q
network location, on the terminal arterioles flow rate and dispersion (coefficient of
variation, CV), and on the terminal arterioles metabolic signal F
mized metabolic activation
, of the input pressurePin,and the transmural
target
, under opti-
mterm
440 6 Network Analysis of Coronary Circulation: II. Pulsatile Flow
https://t.me/med1917
mterm
/s) F
3
mm
-3
(10
term
q
(mmHg)
in
P
mterm
/s) F
3
mm
-3
(10
term
Mean SD CV Mean SD CV Mean SD CV Mean SD CV
q
(mmHg)
in
P
Sub-endocardium Sub-epicardium
/s)
3
mm
3
target
q
(10
1.20 75 1.14 0.26 0.23 0.97 0.08 0.08 75 1.24 0.27 0.22 0.94 0.12 0.12
1.20 100 1.34 0.14 0.11 0.55 0.24 0.44 100 1.44 0.17 0.12 0.33 0.27 0.82
1.20 135 1.48 0.19 0.13 0.19 0.23 1.23 135 2.25 0.74 0.33 0.05 0.16 3.23
0.20 0.10 0.47 0.25 0.53 135 2.19 0.58 0.23 0.49 0.27 1.43
1.80 135 2.05
1.80 100 1.91 0.37 0.19 0.91 0.14 0.16 100 2.03 0.32 0.16 0.53 0.25 0.47
1.80 75 1.18 0.34 0.28 1.00 0.00 0 75 1.34 0.40 0.29 1.00 0.00 0
1.50 165 2.15 0.60 0.28 0.00 0.00 0 165 4.29 1.58 0.37 0.00 0.00 0
1.50 135 1.78 0.15 0.08 0.35 0.25 0.72 135 2.34 0.67 0.29 0.10 0.22 2.12
1.50 100 1.67 0.25 0.15 0.80 0.20 0.25 100 1.72 0.21 0.12 0.64 0.25 0.40
1.50 75 1.18 0.34 0.28 1.00 0.00 0 75 1.32 0.35 0.26 0.99 0.05 0.05
1.20 165 2.15 0.60 0.28 0.00 0.00 0 165 4.29 1.58 0.37 0.00 0.00 0
2.25 75 1.18 0.34 0.28 1.00 0.00 0 75 1.34 0.40 0.29 1.00 0.00 0
1.80 165 2.25 0.20 0.26 0.07 0.18 2.69 165 4.29 1.58 0.37 0.00 0.00 0
2.25 100 2.08 0.51 0.25 0.98 0.06 0.06 100 2.33 0.48 0.21 0.94 0.13 0.14
2.25 135 2.53 0.31 0.12 0.66 0.24 0.36 135 2.82 0.46 0.16 0.35 0.31 0.86
2.25 165 2.74 0.26 0.09 0.28 0.25 0.91 165 4.29 1.58 0.37 0.00 0.00 0
Values of 0 and 1 for the metabolic signal CV imply that the metabolic regulation is exhausted
References 441
https://t.me/med1917
Appendix 7
Effect of myocardium–vessel interaction (MVI) on the metabolic flow reserve
(MFR) in sub-endocardial and sub-epicardial networks under various levels of the
mean input pressureP
P
(mmHg)
in
60 2.43 3.81 3.25
75 2.87 3.44 3.16
90 3.05 3.11 3.06
100 3.13 2.64 2.74
120 3.19 2.28 2.39
135 3.03 2.02 2.13
150 2.76 1.77 1.91
165 2.54 1.53 1.66
180 2.36 1.36 1.45
MFR is the ratio of network flow under full metabolic activation (all F
activation (all F
mterm
in
Sub-endocardium Sub-epicardium
MFR (MVI) MFR (no MVI) MFR (MVI)
¼ 1) to no metabolic
¼ 0). Reproduced from Namani et al. (2018) by permission
mterm
References
Algranati, D., Kassab, G. S., & Lanir, Y. (2010). Mechanisms of coronary vessel/myocardial
interaction. American Journal of Physiology-Heart and Circulatory Physiology, 298(3),
H861–H873. https://doi.org/10.1152/ajpheart.00925.2009
Aliev, M. K., Dos Santos, P., Hoerter, J. A., Soboll, S., Tikhonov, A. N., & Saks, V. A. (2002).
Water content and its intracellular distribution in intact and saline perfused rat hearts revisited.
Cardiovascular Research, 53,48–58. https://doi.org/10.1016/S0008-6363(01)00474-6
Allaart, C. P., & Westerhof, N. (1996). Effect of length and contraction on coronary perfusion in
isolated perfused papillary muscle of rat heart. American Journal of Physiology-Heart and
Circulatory Physiology, 271, H447–H454. https://doi.org/10.1152/ajpheart.1996.271.2.H447
Anrep, G. V., Cruickshank, E. W. H., Downing, A. C., & Sabba Rau, A. (1927). The coronary
circulation in relation to the cardiac cycle. Heart, 14, 111–133.
Arciero, J. C., Carlson, B. E., & Secomb, T. W. (2008). Theoretical model of metabolic blood flow
regulation: Roles of ATP release by red blood cells and conducted responses. American Journal
of Physiology. Heart and Circulatory Physiology, 295(4), H1562–H1571. https://doi.org/10.
1152/ajpheart.00261.2008
Arts, T., Kruger, R. T. I., Van Gerven, W., Lambregts, J. A. C., & Reneman, R. S. (1979).
Propagation velocity and reflection of pressure waves in the canine coronary artery. American
Journal of Physiology, 237, H469–H474. https://doi.org/10.1152/ajpheart.1979.237.4.H469
Austin, R. E., Jr., Aldea, G. S., Coggins, D. L., Flynn, A. E., & Hoffman, J. I. (1990). Profound
spatial heterogeneity of coronary reserve. Discordance between patterns of resting and maximal
myocardial blood flow. Circulation Research, 67(2), 319–331.
Austin, R. E., Smedira, N. G., Jr., Squiers, T. M., & Hoffman, J. I. E. (1994). Influence of cardiac
contraction and coronary vasomotor tone on regional myocardial blood flow. American Journal
442 6 Network Analysis of Coronary Circulation: II. Pulsatile Flow
https://t.me/med1917
of Physiology-Heart and Circulatory Physiology, 266, H2542–H2553. https://doi.org/10.1152/
ajpheart.1994.266.6.H2542
Avolio, A. P. (1980). Multi-branched model of the human arterial system. Medical & Biological
Engineering & Computing, 18, 709–718. https://doi.org/10.1007/BF02441895
Bassingthwaighte, J. B., King, R. B., & Roger, S. A. (1989). Fractal nature of regional myocardial
blood flow heterogeneity. Circulation Research, 65, 578–590. https://doi.org/10.1161/res.65.3.
2766485
Bergfeld, G. R., & Forrester, T. (1992). Release of ATP from human erythrocytes in response to a
brief period of hypoxia and hypercapnia. Cardiovascular Research, 26(1), 40–47.
Beyar, R., Manor, D., Zinemans, D., & Sideman, S. (1993). Chapter C. In Concepts and contro-
versies in modeling the coronary circulation (pp. 135–149). Springer.
Borg, T. K., & Caulfield, J. B. (1979). Collagen in the heart. Texas Reports on Biology and
Medicine, 39, 321–333.
Breisch, E. A., White, F. C., Nimmo, L. E., McKirnan, M. D., & Bloor, C. M. (1986). Exercise-
induced cardiac hypertrophy: A correlation of blood flow and microvasculature. Journal of
Applied Physiology, 60, 1259–1267. https://doi.org/10.1152/jappl.1986.60.4.1259
Brugada, J., Brugada, P., Boersma, L., Mont, L., Kirchhof, C., Wellens, H. J., & Allessie, M. A.
(1991). On the mechanisms of ventricular tachycardia acceleration during programmed electrical stimulation. Circulation, 83, 1621–1629. https://doi.org/10.1161/circ.83.5.2022020
Bruinsma, P., Arts, T., Dankelman, J., & Spaan, J. A. B. (1988). Model of the coronary circulation
based on pressure dependence of coronary resistance and compliance. Basic Reseach in
Cardiology, 83, 510–524. https://doi.org/10.1007/BF01906680
Budel, S., Bartlett, I. S., & Segal, S. S. (2003). Homocellular conduction along endothelium and
smooth muscle of arterioles in hamster cheek pouch: Unmasking an NO wave. Circulation
Research, 93(1), 61–68. https://doi.org/10.1161/01.RES.0000080318.81205.FD
Burnstock, G. (2007). Physiology and pathophysiology of purinergic neurotransmission. Physio-
logical Reviews, 87(2), 659–797. https://doi.org/10.1152/physrev.00043.2006
Campbell, K. S., Patel, J. R., & Moss, R. L. (2003). Cycling cross-bridges increase myocardial
stiffness at submaximal levels of Ca2+ activation. Biophysical Journal, 84(6), 3807–3815.
https://doi.org/10.1016/S0006-3495(03)75108-X
Carlson, B. E., Arciero, J. C., & Secomb, T. W. (2008). Theoretical model of blood flow
autoregulation: Roles of myogenic, shear-dependent, and metabolic responses. American Jour-
nal of Physiology-Heart and Circulatory Physiology, 295, H1572–H1579. https://doi.org/10.
1152/ajpheart.00262.2008
Caulfield, J. B., & Borg, T. K. (1979). The collagen network of the heart. Laboratory Investigation,
40(3), 364–372.
Chadwick, R. S., Tedgul, A., Michel, I. B., Ohayon, I., & Levy, B. I. (1990). Phasic regional
myocardial inflow and outflow: Comparison of theory and experiments. American Journal of
Physiology-Heart and Circulatory Physiology, 258, H1687–HI1698. https://doi.org/10.1152/
ajpheart.1990.258.6.H1687
Chen, K., & Popel, A. S. (2006). Theoretical analysis of biochemical pathways of nitric oxide
release from vascular endothelial cells. Free Radical Biology & Medicine, 41, 668–680. https://
doi.org/10.1016/j.freeradbiomed.2006.05.009
Chilian, W. M., Kuo, L., DeFily, D. V., Jones, C. J., & Davis, M. J. (1993). Endothelial regulation
of coronary microvascular tone under physiological and pathophysiological conditions.
European Heart Journal, 14,55–59.
Chilian, W. M., Layne, S. M., Klausner, E. C., Eastham, C. L., & Marcus, M. L. (1989).
Redistribution of coronary microvascular resistance produced by dipyridamole. American
Journal of Physiology-Heart and Circulatory Physiology, 256, H383–H390. https://doi.org/
10.1152/ajpheart.1989.256.2.H383
Christopoulos, C. (1995). The transmission-line modeling method: TLM. Hoboken, NJ: Wiley-
IEEE Press.
References 443
https://t.me/med1917
Collins, D. M., McCullough, W. T., & Ellsworth, M. L. (1998). Conducted vascular responses:
Communication across the capillary bed. Microvascular Research, 56(1), 43–53. https://doi.
org/10.1006/mvre.1998.2076
Cookson, A. N., Lee, J., Michler, C., Chabiniok, R., Hyde, E., Nordsletten, D. A., ...Smith, N. P.
(2012). A novel porous mechanical framework for modelling the interaction between coronary
perfusion and myocardial mechanics. Journal of Biomechanics, 45, 850–855. https://doi.org/10.
1016/j.jbiomech.2011.11.026
Cornelissen, A. J. M., Dankelman, J., VanBavel, E., & Spaan, J. A. E. (2002). Balance between
myogenic, flow-dependent, and metabolic flow control in coronary arterial tree: A model study.
American Journal of Physiology-Heart and Circulatory Physiology, 282(6), H2224–H2237.
https://doi.org/10.1152/ajpheart.00491.2001
Dankelman, J., Vergroesen, I., Han, Y., & Spaan, J. A. (1992). Dynamic response of coronary
regulation to heart rate and perfusion changes in dogs. The American Journal of Physiology, 263
(2 Pt 2), H447–H452.
Dash, R. K., & Bassingthwaighte, J. B. (2006). Simultaneous blood-tissue exchange of oxygen,
carbon dioxide, bicarbonate, and hydrogen ion. Annals of Biomedical Engineering, 34,
1129–1148. https://doi.org/10.1007/s10439-005-9066-4
Delashaw, J. B., & Duling, B. R. (1991). Heterogeneity in conducted arteriolar vasomotor response
is agonist dependent. The American Journal of Physiology, 260(4 Pt 2), H1276–H1282.
Domeier, T. L., & Segal, S. S. (2007). Electromechanical and pharmacomechanical signalling
pathways for conducted vasodilatation along endothelium of hamster feed arteries. The Journal
of Physiology, 579(Pt 1), 175–186. https://doi.org/10.1113/jphysiol.2006.124529
Doucette, J. W., Goto, M., Flynn, A. E., Husseini, W. K., Jr., & Hoffman, J. I. (1993). Effects of
cardiac contraction and cavity pressure on myocardial blood flow. American Journal of
Physiology-Heart and Circulatory Physiology, 265, H1342–H1352. https://doi.org/10.1152/
ajpheart.1993.265.4.H1342
Douglas, J. E., & Greenfield, J. C. (1970). Epicardial coronary artery compliance in the dog.
Circulation Research, 27, 921–929. https://doi.org/10.1161/01.RES.27.6.921
Downey, J. M., & Kirk, E. S. (1975). Inhibition of coronary blood flow by a vascular waterfall
mechanism. Circulation Research, 36, 753–760. https://doi.org/10.1161/01.RES.36.6.753
Doyle, M. P., & Duling, B. R. (1997). Acetylcholine induces conducted vasodilation by nitric
oxide-dependent and -independent mechanisms. The American Journal of Physiology, 272(3 Pt
2), H1364–H1371.
Duan, B., & Zamir, M. (1992). Viscous damping in one-dimensional wave transmission. The
Journal of the Acoustical Society of America, 92(6), 3358–3363. https://doi.org/10.1121/1.
404185
Duan, B., & Zamir, M. (1995). Pressure peaking in pulsatile flow through arterial tree structures.
Annals of Biomedical Engineering, 23, 794–803. https://doi.org/10.1007/BF02584478
Duncker, D. J., & Bache, R. J. (2008). Regulation of coronary blood flow during exercise.
Physiological Reviews, 88, 1009–1086. https://doi.org/10.1152/physrev.00045.2006
Duza, T., & Sarelius, I. H. (2003). Conducted dilations initiated by purines in arterioles are
endothelium dependent and require endothelial Ca2+. American Journal of Physiology. Heart
and Circulatory Physiology, 285(1), H26–H37. https://doi.org/10.1152/ajpheart.00788.2002
Ellsworth, M. L. (2000). The red blood cell as an oxygen sensor: What is the evidence? Acta
Physiologica Scandinavica, 168(4), 551–559. https://doi.org/10.1046/j.1365-201x.2000.00708.
x
Ellsworth, M. L., Forrester, T., Ellis, C. G., & Dietrich, H. H. (1995). The erythrocyte as a regulator
of vascular tone. The American Journal of Physiology, 269(6 Pt 2), H2155–H2161.
Emerson, G. G., & Segal, S. S. (2000). Endothelial cell pathway for conduction of hyperpolariza-
tion and vasodilation along hamster feed artery. Circulation Research, 86(1), 94–100.
Farias, M., III, Gorman, M. W., Savage, M. V., & Feigl, E. O. (2005). Plasma ATP during exercise:
Possible role in regulation of coronary blood flow. American Journal of Physiology. Heart and
Circulatory Physiology, 288(4), H1586–H1590. https://doi.org/10.1152/ajpheart.00983.2004
444 6 Network Analysis of Coronary Circulation: II. Pulsatile Flow
https://t.me/med1917
Feigl, E. O. (1983). Coronary physiology. Physiological Reviews, 63,1–205. https://doi.org/10.
1152/physrev.1983.63.1.1
Fibich, G., Lanir, Y., & Liron, N. (1993). Mathematical model of blood flow in a coronary capillary.
American Journal of Physiology-Heart and Circulatory Physiology, 265, H1829–H1840.
https://doi.org/10.1152/ajpheart.1993.265.5.H1829
Figueroa, X. F., Chen, C. C., Campbell, K. P., Damon, D. N., Day, K. H., Ramos, S., & Duling,
B. R. (2007). Are voltage-dependent ion channels involved in the endothelial cell control of
vasomotor tone? American Journal of Physiology. Heart and Circulatory Physiology, 293(3),
H1371–H1383. https://doi.org/10.1152/ajpheart.01368.2006
Figueroa, X. F., & Duling, B. R. (2008). Dissection of two Cx37-independent conducted vasodi-
lator mechanisms by deletion of Cx40: Electrotonic versus regenerative conduction. American
Journal of Physiology. Heart and Circulatory Physiology, 295(5), H2001–H2007. https://doi.
org/10.1152/ajpheart.00063.2008
Fokkema, D. S., VanTeeffelen, J. W., Dekker, S., Vergroesen, I., Reitsma, J. B., & Spaan, J. A.
(2005). Diastolic time fraction as a determinant of subendocardial perfusion. American Journal
of Physiology-Heart and Circulatory Physiology, 288, H2450–H2456. https://doi.org/10.1152/
ajpheart.00790.2004
Formaggia, L., Gerbeau, J. F., Nobile, F., & Quarteroni, J. (2001). On the coupling of 3D and 1D
Navier-Stokers equations for flow problems in compliant vessels. Computer Methods in Applied
Mechanics and Engineering, 191, 561–582. https://doi.org/10.1016/S0045-7825(01)00302-4
Fung, Y. C. (1984). Biodynamics: Circulation. New York: Springer.
Furchgott, R. F., & Zawadzki, J. V. (1980). The obligatory role of endothelial cells in the relaxation
of arterial smooth muscle by acetylcholine. Nature, 288(5789), 373–376.
Gan, R. Z., & Moodie, T. B. (1989). Broad-spectrum impedance calculation for arterial models.
International Journal of Engineering Science, 27,63–76. https://doi.org/10.1016/0020-7225
(89)90168-7
Gan, R. Z., & Yen, R. T. (1994). Vascular impedance analysis in dog lung with detailed morpho-
metric and elasticity data. Journal of Applied Physiology, 77, 706–717. https://doi.org/10.1152/
jappl.1994.77.2.706
Gao, J., Huang, W., & Yen, R. T. (2000). Microcirculation impedance analysis in cat lung. Journal
of Biomechanical Engineering, 122,99–103. https://doi.org/10.1115/1.429647
Gerdes, A. M., & Kasten, F. H. (1980). Morphometric study of endomyocardium and
epimyocardium of the left ventricle in adult dogs. American Journal of Anatomy, 159,
389–394. https://doi.org/10.1002/aja.1001590405
Giezeman, M. J., VanBavel, E., Grimbergen, C. A., & Spaan, J. A. (1994). Compliance of isolated
porcine coronary small arteries and coronary pressure-flow relations. American Journal of
Physiology-Heart and Circulatory Physiology, 36, H1190–H1198. https://doi.org/10.1152/
ajpheart.1994.267.3.H1190
Goldman, D., Fraser, G. M., Ellis, C. G., Sprague, R. S., Ellsworth, M. L., & Stephenson, A. H.
(2012). Toward a multiscale description of microvascular flow regulation: O(2)-dependent
release of ATP from human erythrocytes and the distribution of ATP in capillary networks.
Frontiers in Physiology, 3, 246. https://doi.org/10.3389/fphys.2012.00246
Goodwill, A. G., Dick, G. M., Kiel, A. M., & Tune, J. D. (2017). Regulation of coronary blood
flow. Comprehensive Physiology, 7, 321–382. https://doi.org/10.1002/cphy.c160016
Gorman, M. W., & Feigl, E. O. (2012). Control of coronary blood flow during exercise. Exercise
and Sport Sciences Reviews, 40(1), 37–42. https://doi.org/10.1097/JES.0b013e3182348cdd
Gorman, M. W., Ogimoto, K., Savage, M. V., Jacobson, K. A., & Feigl, E. O. (2003). Nucleotide
coronary vasodilation in guinea pig hearts. American Journal of Physiology. Heart and Circu-
latory Physiology, 285(3), H1040–H1047. https://doi.org/10.1152/ajpheart.00981.2002
Gorman, M. W., Rooke, G. A., Savage, M. V., Jayasekara, M. P., Jacobson, K. A., & Feigl, E. O.
(2010). Adenine nucleotide control of coronary blood flow during exercise. American Journal
of Physiology. Heart and Circulatory Physiology, 299(6), H1981–H1989. https://doi.org/10.
1152/ajpheart.00611.2010
Соседние файлы в папке Библиотека им академика М.И. Перельмана