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Intersample fluctuations in phosphocreatine concentration determined by <sup>31</sup>P‐magnetic resonance spectroscopy and parameter estimation of metabolic responses to exercise in humans

2000· article· en· W2086206537 on OpenAlexaff
Harry B. Rossiter, Franklyn A. Howe, Susan Ward, John M. Kowalchuk, John R. Griffiths, Brian J. Whipp

Bibliographic record

VenueThe Journal of Physiology · 2000
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsWestern University
Fundersnot available
KeywordsPhosphocreatineChemistryAmplitudeNuclear magnetic resonanceAnalytical Chemistry (journal)Standard deviationPhysicsInternal medicineEnergy metabolismMathematicsChromatographyMedicineStatistics

Abstract

fetched live from OpenAlex

The ATP turnover rate during constant-load exercise is often estimated from the initial rate of change of phosphocreatine concentration ([PCr]) using 31P-magnetic resonance spectroscopy (MRS). However, the phase and amplitude characteristics of the sample-to-sample fluctuations can markedly influence this estimation (as well as that for the time constant (tau) of the [PCr] change) and confound its physiological interpretation especially for small amplitude responses. This influence was investigated in six healthy males who performed repeated constant-load quadriceps exercise of a moderate intensity in a whole-body MRS system. A transmit- receive surface coil was placed under the right quadriceps, allowing determination of intramuscular [PCr]; pulmonary oxygen uptake (VO2) was simultaneously determined, breath-by-breath, using a mass spectrometer and a turbine volume measuring module. The probability density functions (PDF) of [PCr] and VO2 fluctuations were determined for each test during the steady states of rest and exercise and the PDF was then fitted to a Gaussian function. The standard deviation of the [PCr] and VO2 fluctuations at rest and during exercise (sr and sw, respectively) and the peak centres of the distributions (xc(r) and xc(w)) were determined, as were the skewness (gamma1) and kurtosis (gamma2) coefficients. There was no difference between sr and sw for [PCr] relative to the resting control baseline (s(r) = 1.554 %delta (s.d. = 0.44), s(w) = 1.514 %delta (s.d. = 0.35)) or the PDF peak centres (xc(r) = -0.013 %delta (s.d. = 0.09), xc(w) -0.197 %delta (s.d. = 0.18)). The standard deviation and peak centre of the 'noise' in VO2 also did not vary between rest and exercise (sr = 0.0427 l min(-1) (s.d. = 0.0104), s(w) = 0.0640 l min(-1) (s.d. = 0.0292); xc(r) = -0.0051 l min(-1) (s.d. = 0.0069), xc(w) 0.0022 l min(-1) (s.d. = 0.0034)). Our results demonstrate that the intersample 'noise' associated with [PCr] determination by 31P-MRS may be characterised as a stochastic Gaussian process that is uncorrelated with work rate, as previously described for VO2. This 'noise' can significantly affect the estimation of tau[PCr] and especially the initial rate of change of [PCr], i.e. the fluctuations can lead to variations in estimation of the initial rate of change of [PCr] of more than twofold, if the inherent 'noise' is not accounted for. This 'error' may be significantly reduced in such cases if the initial rate of change is estimated from the time constant and amplitude of the response.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.269
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations66
Published2000
Admission routes1
Has abstractyes

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