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O2 and heart rate
kinetics in cycling: transitions from an elevated baseline
Exercise Physiology Laboratory, Department of Nutrition, Food and Exercise Sciences, Florida State University, Tallahassee Florida 32306
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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The purpose of this
study was to examine oxygen consumption
(
O2) and heart rate kinetics during
moderate and repeated bouts of heavy square-wave cycling from an
exercising baseline. Eight healthy, male volunteers performed
square-wave bouts of leg ergometry above and below the gas exchange
threshold separated by recovery cycling at 35%
O2 peak.
O2 and heart rate kinetics were modeled,
after removal of phase I data by use of a biphasic on-kinetics and
monoexponential off-kinetics model. Fingertip capillary blood was
sampled 45 s before each transition for base excess,
HCO
oxygen uptake kinetics; cycling
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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AT THE ONSET OF
AN ABRUPT INCREASE in work rate (square-wave exercise), ATP
consumption rises immediately, whereas oxygen consumption
(O2) increases more slowly. What keeps
O2 from rising immediately to its
steady-state level? Are the kinetics a function of metabolic inertia,
or is delivery of oxygen to blame? Below the gas-exchange threshold
(GET; moderate exercise), metabolic inertia is believed the
limiting factor (17, 19). Controversy remains for
transitions above the GET (heavy exercise), where O2 kinetics are more complex.
In heavy exercise, the kinetics incorporate at least two components
[beyond the initial phase I component (32)] that
manifest in series (4). An intrinsic property of a system
where components manifest in series is that the mean response time may
be altered by changes to the component time constants, amplitudes,
and/or time delays. For example, the mean response time may decrease by
decreasing the component time constants without changing component amplitudes, by changing the ratio of the amplitudes with no change in
the time constants, or by an earlier slow-component onset despite invariant time constants and amplitudes. Therefore, the conclusions of
previous studies that were focused on the mean response time (7,
15, 23) are not easily applied to understanding the underlying
physiology of such a complex system. Without a detailed analysis of the
O2 kinetic components, the issue of
metabolic inertia or oxygen delivery for heavy exercise remains unresolved.
This study was driven by the hypothesis that the rate of increase in
O2 in heavy square-wave exercise is set
by metabolic state (inertia and demand) and is not limited by the
ability of the cardiovascular system to deliver oxygen in healthy
adults. Therefore, it was postulated that 1) there would be
no difference in the initial (fast component) time constant among
moderate and repeated heavy transitions; 2) systemic
acidosis would not be a necessary condition for the speeded mean
response time in the second heavy bout (acidosis was previously
implicated in improving perfusion and we hypothesized perfusion is not
the limiting factor); and 3) cardiac output kinetics could
be dissociated from the O2 response.
To this end, an elevated baseline (~35%
O2 peak, ~60% GET) was used, which
differs from the typically employed unloaded cycling baseline. The
elevated baseline was used, in part, to enhance recovery (addressing
hypothesis 2); an active recovery of this moderate intensity
is optimal for speeding systemic metabolic recovery (13).
Moreover, an elevated baseline may slow heart rate kinetics
(21) and was used in this study for its potential to
dissociate O2 and heart rate during the
nonsteady state (addressing hypothesis 3). The elevated
baseline was expected to raise baseline cardiac output, setting stroke
volume closer to its plateau (25) and to allow more
reliable inferences to cardiac output kinetics directly from the heart
rate response (also addressing hypothesis 3). This work rate
(~35% O2 peak, ~60% GET) has been shown to maximize parasympathetic withdrawal, leaving the slower, sympathetic nervous system to mediate increases in heart rate and
cardiac output (33).
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METHODS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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Subjects. Eight healthy male volunteers experienced with cycling on a stationary ergometer gave written informed consent to participate in this study. The procedures were approved by the Florida State University Human Subjects Review Board. Subjects were 27 ± 3 yr old, 177 ± 4 cm tall, and 72 ± 8 kg in body mass.
Preparation. Subjects were prohibited from alcohol and strenuous activity for 24 h and from caffeine for 15 h before arrival in the laboratory. No one reported taking dietary supplements or ergogenic aids aside from vitamin/mineral supplements. Subjects consumed a light carbohydrate meal 2-3 h before arrival in the laboratory and repeated this meal before all test days. Testing was at the same time of day, ±2 h, for each subject. Subjects were not permitted to cycle to the laboratory and remained sedentary in the testing area for at least 30 min before each test.
Testing. Subjects cycled at 90 rpm on an electrically braked leg ergometer (Lode, Groningen, Netherlands) on 4 separate testing days. Ninety revolutions per minute was the approximate mean preferred cadence among the subjects and the one most often chosen on a blinded familiarization day. Cadence differences appear to alter the kinetics response only to a small degree within the typically employed range (3).
Gas exchange was measured every breath with a Parvomedics MMS-2400 system (Consentius Technologies, Salt Lake City, UT). Total dead space of the system (mouthpiece, valve, collection tube, pneumotach, mixing chamber, and sampling tube) was 4.98 liters. A seven-point flowmeter calibration was made before each test with a 3-liter syringe (Hans Rudolph, Kansas City, MO) at rates that spanned the expected measurements. Gas calibration was made immediately before each test, with gases spanning the range of O2 and CO2 expected during data collection. The first day was a test forO2 peak (1 W/5 s) and continued until the cadence could not be maintained despite
verbal encouragement. The GET was defined as the break in slope of the CO2 production
(CO2)-O2
relationship by use of Levenberg-Marquadt estimation to identify the
intersection of two lines that minimized the sum of the squared
residuals. Starting values in the iteration procedures were 1.0 for the
initial slope, threshold estimated visually (30), and 1.3 for the upper slope. The computer-identified GET was, in all cases, in
close agreement with the visually identified CO2-O2 break.
The last three sessions were divided into one moderate (Mod) and two
heavy (Hvy) exercise days, which were completed in random order. On
each day, after a 5-min baseline warm-up at a
O2 of 35% peak, subjects completed two
10-min cycling periods at an elevated work rate, separated by 10 min of
baseline recovery cycling. The elevated work rate was either a
O2 of 90% GET (Mod) or a O2 of 30% of the difference between GET
and O2 peak (30%
; Hvy). Test
sessions for a given subject were separated by 
2 days.
The O2 responses for the 2 Hvy
transition days were time aligned. To remove nonphysiological datum
points resulting from coughing, sneezing, etc., any breaths more than
four standard deviations away from the mean of the surrounding six
breaths (3 before and 3 after) were deleted. The decision to remove
these points was confirmed visually to ensure that only clearly
nonphysiological outliers were deleted; these amounted to about six to
eight breaths over the 10-min period for each test. The superimposed
data set was then smoothed (rolling five-breath average).
Because the elevated baseline is different from the unloaded cycling
baseline in previous studies (15, 23), we preliminarily analyzed the two Mod bouts separately. There was no difference (paired
t-test) between the two Mod bouts for any on- or off-kinetic parameter (P > 0.05). Therefore, the two Mod bouts,
completed on the same day, were time aligned and averaged to produce a
single response, which enhances confidence in model fitting
(22).
Before modeling, each test was examined for a steady state. Accurate
and complete kinetic modeling is not assured without establishing, or
rigorously estimating, a steady state for the parameter(s) to be
modeled. Linear regression was applied to the final 3 min of data for
each average response. The 95% confidence interval for each regression
slope was examined; in all cases, the 95% confidence interval included
zero. This was the basis for deciding that a steady state had been
reached. To further confirm the steady state, the 9- to 10-min average
O2 was compared with the modeled
asymptote (paired t-test) and was not different (P > 0.05).
As described by Whipp and colleagues (32), the phase I
component (9) was removed before modeling by visually
analyzing the O2 and respiratory
exchange ratio (RER) responses for each transition. Initially,
O2 kinetics were modeled using a
monoexponential formula (Eq. 1) to compare these
results with the faster mean response time (MRT) reported previously
(7, 15, 23). The MRT was calculated as
TD1 + 
1. After a significant speeding
of the overall kinetics (smaller MRT in the second bout) was found, gas
exchange data were modeled using a biphasic formula with independent time delays (Eq. 2)
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(1) |
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(2) |
O2(t) is whole
body O2 at time t, B is
baseline (warm-up) O2 calculated as the
average O2 over the last 2 min of
warm-up, A1 and A2 are the fast and
slow-component amplitudes, respectively, TD1 and
TD2 are their respective time delays, and 1
and 2 are their respective time constants. Invariably,
the slow component (A2) regressed toward zero in the Mod
transitions; therefore, the Mod bouts were reanalyzed using a
monoexponential formula (Eq. 1).
Each amplitude component was allowed to begin only after its time
delay. Confidence in the time constant (1) was 2.23 ± 0.41 s in Hvy1, 2.34 ± 0.45 s in
Hvy2, and 4.53 ± 0.89 s in Mod, based on
formulas reported by Lamarra and colleagues (22).
Off-kinetics were initially modeled using a biphasic formula; however,
the model consistently regressed to a monoexponential function of time
(the time constants for the two components were not different).
Therefore, the off-kinetics were modeled using a monoexponential
equation
|
(3) |
O2 is end-exercise
O2 with the other parameters as
described above; the prime mark (') designates these as off-kinetics parameters.
Heart rate (HR) was monitored telemetrically (Polar Electro, Woodbury,
NY) and recorded online at the end of every breath (signaled from the
pneumotach). The data were then superimposed, averaged, filtered, and
modeled in the identical manner as for the
O2 data. Therefore, the heavy
transitions were modeled much like the method used by Engelen and
colleagues (14). The justification for choosing the
biphasic model is that visual analysis clearly revealed a fast phase
approaching a plateau with a subsequent delayed rise. These response
characteristics were particularly clear after averaging of the two responses.
As described by Stringer and colleagues (29),
cardiac output (
) was estimated as = O2/[5.721 + (0.1047 × %O2 peak)]. Stroke volume (SV) was
estimated as SV = /HR.
Capillary blood was taken from a fingertip 45 s before each of the
two abrupt increases in work rate. Blood lactate (Accusport, Indianapolis, IN), pH, base excess, and [HCO1
point above and 1 point below the expected measurement).
Gains for the responses were calculated as the fast-component gain,
G1 = A1/W, and total gain,
GT = (A1+A2)/W.
Statistics.
Paired t-tests were used to compare the initial MRTs between
the two Hvy transitions. Paired t-tests were used to compare A1, A2, TD2, 2,
'A1, G1, base excess,
[HCOO2, baseline HR, TD1, 1, GT, 'TD1,
and '1 among the Mod and two Hvy transitions; subjects
served as blocks, the category of comparison as the fixed factor, and
the measurement as the dependent variable. Tukey's post hoc
comparisons were used whenever overall significance was found to
identify the differing pairs. Paired t-tests were used to
compare time delays and time constants between the
O2 and HR responses. Pearson moment
correlations were computed for each bout (Mod, Hvy1,
Hvy2) between
O21 and HR
1. Alpha was set at P = 0.05.
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RESULTS |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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O2 peak and GET were 60 ± 3 ml
O2 · kg
1 · min1
and 58 ± 7% peak, respectively. Blood lactate and pH returned to
baseline before the onset of the second bout ([La]:
1st = 1.78 ± 0.34 mM , 2nd = 2.24 ± 0.64 mM; pH: 1st = 7.38 ± 0.01, 2nd = 7.38 ± 0.02; mean ± SD). In contrast, base excess and
[HCO
0.96 ± 1.11 mM; [HCO
Actual work rates were baseline 35.6 ± 5.2%
O2 peak or 60.5 ± 10.2% GET, Mod
asymptote 52.2 ± 5.9% O2 peak or
89.9 ± 7.2% GET, and Hvy asymptote 69.4 ± 7.1%
O2 peak or 27.2 ± 13.2% .
O2.
Initial analysis of mean response times revealed a significant speeding
of the overall kinetics in Hvy2 (MRT: 1st = 55.5 ± 10.1 s, 2nd = 44.3 ± 7.7 s). The on-transition
parameters are given in Table 1.
Subject 6, for whom the first Hvy bout generated a
slow-component amplitude of 92 ml O2/min, demonstrated
monoexponential kinetics in the second bout (A2 regressed
to zero). Therefore, comparisons between the two bouts for
O2 slow-component parameters (A2, TD2, 2) were made with
seven subjects. The amplitude and gain of the fast component
(A1, A1/W) were significantly larger than in
the first bout, as was the overall projected asymptote for the fast
component (B + A1). The time constant of the fast component was not different among Mod, Hvy1, and
Hvy2. The amplitude of the slow component (A2)
was significantly smaller in Hvy2 than in Hvy1.
The onset of the slow component (TD2) was significantly later in Hvy2 and its time constant (2) was
significantly smaller than in Hvy1. The
O2 asymptote and overall gain
(GT) was not different between Hvy1 and
Hvy2. The two Hvy bouts for subject 5 are shown
superimposed in Fig. 1.
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HR.
The on-transition kinetics of the heart rate responses are shown in
Table 1. For subject 7, the kinetics were monoexponential in
both Hvy bouts. The time delay (TD2) for the slow component of the HR kinetics was significantly longer in Hvy2 than in
Hvy1 and was not significantly different from
TD2 for O2 in either bout.
The initial rise in HR (A1) for the two Hvy bouts was not different, and its time constant was not different among the Mod and
the two Hvy bouts. However, in each case, the time constant (1) was significantly longer than for
O2. Furthermore, the HR and
O2 fast-component time constants
(1) were not significantly correlated (Fig.
2). The slow component of HR kinetics had
a smaller amplitude and time constant in Hvy2 than in
Hvy1; the time constant was not significantly different
from the O22 for the same
bout. There was a significantly lower HR asymptote in Hvy2
than in Hvy1.
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O2 in the same bouts.
Estimated and SV comparisons are given in Fig.
3. Estimated stroke volume was not
different across conditions; the elevated baseline appeared to serve
its purpose in raising stroke volume to a plateau. Therefore, it is
assumed that HR was responsible for any changes in and that HR
kinetics reflect kinetics.
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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The primary finding in the present study was that a faster MRT in
repeated heavy transitions is not due to a smaller fast-component time
constant but to a relative shift in kinetic amplitudes (larger A1 and smaller A2) with the same final
asymptote. It was further demonstrated that HR (and presumably )
kinetics can be dissociated from O2
kinetics with a baseline of ~35%
O2 peak.
Burnley and colleagues (8) modeled repeated heavy
transitions from a 20-W baseline and demonstrated a greater
fast-component asymptote with invariant time constant (~25 s). Thus
the faster MRT in repeated heavy transitions does not appear to be the
result of faster initial kinetics regardless of baseline work rate.
This leads to the conclusion that factors limiting 1 are
not different in repeated bouts.
It has been demonstrated that previous leg (7, 15, 23) and previous arm (7) exercise results in a faster MRT in a subsequent bout of heavy leg ergometry. If a shift in the relative amplitudes is the cause of the faster MRT, then what causes these changes even when the previous exercise is in a different muscle group?
A potential mechanism for shifts in kinetic component amplitudes. Bangsbo and colleagues (1) reported that previous heavy arm exercise caused a greater potassium loss from subsequently exercising legs than in the control (no previous arm exercise) condition and concluded that elevated potassium concentration in the leg interstitium was an important mediator of fatigue. We hypothesize that previous heavy exercise (arm or leg) disrupts the sarcolemmal electrochemical gradient through elevated extracellular potassium concentration in the leg interstitium, as reported by Bangsbo and colleagues. The resulting fatigue would demand the recruitment of additional, potentially less economic fibers to begin the subsequent bout and mediate a greater fast-component asymptote, as modeled in the present study and recently by others (8).
Oxygen demand over the pre-TD2 period is no greater than the observed A1 asymptote (5). Consistent with this, Grassi and colleagues (16) demonstrated that elevating pretransition O2 delivery along with adenosine infusion (to induce vasodilation) in maximally stimulated mammalian muscle speeds1 without altering A1. These demonstrations support the conclusion that the
greater A1 in Hvy2 was the result of a greater
oxygen demand (potentially from additionally recruited fibers), not the
removal of a delivery limitation. Dispersing the same work rate over a
larger motor unit pool at the onset of Hvy2 would lead to
less fatigue of individual myocytes, demanding a smaller additional
recruitment in the slow-component period to maintain force
output. This is supported by the present data showing 1) a
later slow-component onset (consistent with a better ability to sustain
the initial change in work rate without fatigue), 2) a
smaller slow-component time constant (consistent with a reduced need to
serially recruit new myocytes with developing fatigue), and
3) a smaller slow-component amplitude (consistent with a
smaller net increase in motor unit recruitment). These considerations
are consistent with the prevailing theory that the slow component is
the result of increased motor unit recruitment (3, 6, 28,
31).
In contrast to the present study, Burnley and colleagues
(8) found the overall asymptote to be lower in the second
bout. This is most likely a methodological difference; Burnley and
colleagues (8) used 6-min bouts resulting in termination
of the exercise at a time when only ~50% of the apparent slow
component had developed (before one time constant had elapsed). The
10-min bouts in the present study included >85% of the slow-component
data (i.e., >22), and the
O2 time slope was not different from
zero over the last minutes of each bout. Short bouts may not allow for
a full adjustment to the work rate and may lead to inaccurate modeling. However, it is possible that differences in the two studies exist that
facilitated a truly smaller asymptote in their study (8), but these must await further investigation. Because the final asymptote
was not different across bouts in the current study, it is assumed that
the same final motor unit pool was recruited (or its metabolic
equivalent) and that the primary difference between the two bouts was
in the partitioning of recruitment order.
An elevated baseline may speed O2
kinetics (11, 12), although this is not a universal
conclusion (21). An intriguing finding in the present
study was the similar 1 for moderate and heavy
transitions (Table 1). In contrast, 1 is usually
significantly slower in heavy compared with moderate transitions;
note that we used a baseline of moderate exercise, whereas
previous studies have used a light or unloaded baseline (8, 14,
24). Grassi and colleagues (16) showed that
elevated pretransition oxygen availability could significantly speed
1 from ~25 to ~18 s. These values are remarkably
similar to the 1 of ~19 s in the present study with an
elevated baseline and the ~25-s 1 generally reported using a light or unloaded baseline (2-4, 8, 14, 24).
It is possible that our elevated baseline facilitated a pretransition increase in oxygen availability for newly recruited motor units. A
mechanism for this is illustrated by the elegant work of Segal and
colleagues (for reviews, see Refs. 26 and 27), which has demonstrated local vasoactive metabolites may activate upstream vasodilation. Due to the structure of the capillary bed, this vasodilation increases oxygen availability to both active and inactive
fibers. Should the inactive fibers be required for the subsequent
increase in work rate, they would then have sufficient oxygen to
rapidly accelerate oxidative metabolism, potentially speeding
1. However, there is variability among laboratories and
subjects larger than the ~18- to ~25-s differences suggested here.
Thus it is emphasized that these ideas are speculation only and deserve
further research.
HR kinetics. The present results are consistent with the ability to slow HR kinetics by using an elevated baseline work rate (20, 21). The elevated baseline effectively removes the more rapid influence of the parasympathetic system (33).
The complexity of the HR response to heavy square-wave transitions has previously been observed (14, 18) and modeled into fast and slow components as in the present study (14). Engelen and colleagues (14) showed that HR1 was
slower than O21 by ~15
s, although no statistical comparison was given. Significant dissociation of HR1 (and presumably cardiac output
1) and O21 in the present study emphasizes the importance of local blood flow
control in matching oxygen demand and delivery during the nonsteady state.
Off-kinetics. Because the off-kinetics reduced to a monoexponential function, the two phases of the on-kinetics (the fast and slow phases) appear to have equivalent and coincident recovery profiles, at least to the extent that mathematical modeling could detect a difference in these subjects. The off-kinetics time delays and time constants were not different among the Mod and Hvy bouts.
A recent study with the use of unloaded pedaling as a baseline and designed to investigate this issue directly has also concluded that the off-kinetics ofO2 are "independent of
the magnitude of the contribution to the slow phase from the
on-transient kinetics" (10). These investigators found
the off-kinetics well fit by including a slow phase, beginning with the
fast phase, that had a small amplitude and large time constant. We did
not find a slow phase component to the off-kinetics. The difference may
be due to either the different baselines or the longer recovery time in
the study of Cunningham and colleagues (10) (15 min vs.
our 10 min). Because our elevated baseline is known to speed recovery (13), it is more likely that baseline work rate was the
culprit. Thus the slow phase of recovery may be representative of
metabolic processes still demanded at our moderate intensity and
therefore not a part of the recovery profile in this study.
Nevertheless, the two studies are in agreement that whatever causes the
slow component appears to begin recovery immediately upon cessation of
the exercise, recovers more rapidly than it developed and does so in
parallel with the fast component of recovery.
In conclusion, repeated bouts of heavy exercise in this study resulted
in a smaller MRT, mediated by an increase in the fast-component amplitude, similar fast-component time constant, and similar final steady state; systemic acidosis was not necessary for the faster MRT.
HR (and presumably ) kinetics may be slowed and dissociated from
O2 kinetics with a baseline of mild
exercise. Most important, these data, in conjunction with previous
research, are consistent with a role for local oxygen delivery
limitation at the onset of heavy exercise transitions, which is not
lifted by repeated bouts but may be reduced using a preparatory
baseline of moderate exercise. A role for membrane potential, motor
unit recruitment patterns, and pretransition vasodilation was proposed
to underlie the O2 kinetics.
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FOOTNOTES |
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Address for reprint requests and other correspondence: R. J. Moffatt, 436 Sandels Bldg., Dept. of Nutrition, Food and Exercise Sciences, Florida State University, Tallahassee, FL 32306 (E-mail: rmoffatt{at}mailer.fsu.edu).
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 16 May 2000; accepted in final form 16 January 2001.
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REFERENCES |
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TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES
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