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Muscle phosphorus magnetic resonance spectroscopy oxidative indices correlate with physical activity

2000· article· en· W2148332363 on OpenAlexaff
Maria Carmela Tartaglia, Jacqueline T. Chen, Zografos Caramanos, Tanja Taivassalo, Douglas L. Arnold, Zohar Argov

Bibliographic record

VenueMuscle & Nerve · 2000
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsPhosphocreatineDeconditioningOxidative phosphorylationInternal medicineChemistryEndocrinologyOxidative metabolismSkeletal musclePhosphomonoestersNuclear magnetic resonance spectroscopyPhosphorusNuclear magnetic resonanceMedicineMetabolismEnergy metabolismBiochemistry

Abstract

fetched live from OpenAlex

The purpose of this study was to assess the effect of physical deconditioning on skeletal muscle's oxidative metabolism as evaluated by phosphorus-31 magnetic resonance spectroscopy ((31)P MRS). Twenty-seven subjects without muscle disease, representing a wide range of fitness levels, were evaluated with (31)P MRS. Spectra were obtained at rest and during recovery from in-magnet exercise. The data show a significant correlation between maximum resting metabolic equivalent (MET) score and the following (31)P MRS recovery indices: adenosine diphosphate and phosphocreatine recovery half-time; initial phosphocreatine resynthesis rate; calculated estimation of mitochondrial capacity; pH at end of exercise; and phosphocreatine depletion. In addition, significant differences between the deconditioned and conditioned group were found for all of the aforementioned recovery indices. At rest, only the inorganic phosphate concentration was significantly different between the two groups. These data indicate that physical activity level should be taken into account when assessing patients' oxidative metabolism with (31)P MRS.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.702

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.280
Teacher spread0.269 · 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 designOther design
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

Citations24
Published2000
Admission routes1
Has abstractyes

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