MétaCan
Menu
← Back to cohort
Record W2277037387

Abstract 15119: Remote Ischemic Preconditioning Does Not Affect Skeletal Muscle Energy Metabolism Measured by 31-Phosphorus Magnetic Resonance Spectroscopy: A Randomized Crossover Trial

2013· article· en· W2277037387 on OpenAlexaff
Emilie Jean‐St‐Michel, Jessica E. Caterini, S. H. Thompson, Cedric Manlhiot, Brian W. McCrindle, Andrew N. Redington, Greg D. Wells

Bibliographic record

VenueCirculation · 2013
Typearticle
Languageen
FieldMedicine
TopicAdvanced MRI Techniques and Applications
Canadian institutionsUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsMedicineIschemic preconditioningIschemiaPhosphocreatinePerfusionCardiologySkeletal muscleInternal medicineVO2 maxEnergy metabolismAnesthesiaHeart rateBlood pressure
DOInot available

Abstract

fetched live from OpenAlex

Background: Remote ischemic preconditioning (RIPC), induced by transient limb ischemia, has been demonstrated to protect organs against ischemia-reperfusion injury and to improve maximal, but not submaximal exercise performance. Nonetheless, the cellular mechanisms remain poorly understood. Phosphocreatinine concentration was recently shown to recover more rapidly during reperfusion in muscle that has undergone local ischemic preconditioning. Therefore, we used 31P-MRS and blood oxygen level dependent MRI (BOLD fMRI) to test the hypothesis that RIPC beneficially modifies muscle metabolism and perfusion during maximal but not submaximal exercise. Method: Ten healthy subjects, 20 to 27 years, were randomised to RIPC (4 cycles of 5 minutes (min) arm ischemia/5 min reperfusion) or sham procedure, then crossed-over to the other intervention. Each subject performed ten 30 seconds (sec) bouts at 65% of the previously determined maximal work rate each separated by 15 sec of rest. Finally, six of the subjects also performed two bouts of 60 sec of maximal (100%) exercise. 31P-MRS and BOLD fMRI data were obtained before and after every testing protocol. Results: There were no differences for ATP production (0.3±0.1 RIPC mmol/L, 0.3±0.1 SHAM, p=0.90), phosphocreatinine to inorganic phosphate ratio (0.9±0.8, 1.0±0.7, p=0.78) or in the half-time of phosphocreatinine recovery (27.1±11.8, 24.6±12.8 sec, p=0.66) during submaximal exercise. There were also no differences in BOLD signal amplitude between RIPC and SHAM (279.3 ± 169.3, 276.6 + 135.9, p=0.96) or for BOLD signal intercept (1614.0 + 373.4, 1522.7 + 332.9, p = 0.1), or for the BOLD signal recovery time constant (75.5 + 154.5, 47.3 + 23.6 sec, p=0.34). Similarly for the maximal exercise protocol, we observed no difference in pre vs. post-exercise pH (0.77±0.26, 0.93±0.14, p= 0.29), post-exercise phosphocreatinine and phosphate ratio (1.9±1.5, 2.4±2.4, p=0.70) or in the half-time of phosphocreatinine recovery (23.0±19.5, 20.2±7.3 sec, p=0.67). Conclusion: The lack of demonstrated effect of RIPC on aerobic and anaerobic energy metabolism, and perfusion, is different than previously shown with local preconditioning and may suggest that different mechanisms play a role in the observed effects of RIPC.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0130.002

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 head0.010
GPT teacher head0.271
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCirculation→Same topicAdvanced MRI Techniques and Applications→French-language works237,207→