Perfusion has no effect on the <i>in vivo</i> CEST effect from Cr (CrCEST) in skeletal muscle
Bibliographic record
Abstract
Abstract Creatine, a key component of muscle energy metabolism, exhibits a chemical exchange saturation transfer (CEST) effect between its amine group and bulk water, which has been exploited to spatially and temporally map creatine changes in skeletal muscle before and after exercise. In addition, exercise leads to an increase in muscle perfusion. In this work, we determined the effects of perfused blood on the CEST effects from creatine in skeletal muscle. Experiments were performed on healthy human subjects (n = 5) on a whole‐body Siemens 7T magnetic resonance imaging (MRI) scanner with a 28‐channel radiofrequency (RF) coil. Reactive hyperemia, induced by inflation and subsequent deflation of a pressure cuff secured around the thigh, was used to increase tissue perfusion whilst maintaining the levels of creatine kinase metabolites. CEST, arterial spin labeling (ASL) and 31P MRS data were acquired at baseline and for 6 min after cuff deflation. Reactive hyperemia resulted in substantial increases in perfusion in human skeletal muscle of the lower leg as measured by the ASL mean percentage difference. However, no significant changes in CrCEST asymmetry (CrCESTasym) or 31P MRS‐derived PCr levels of skeletal muscle were observed following cuff deflation. This work demonstrates that perfusion changes do not have a major confounding effect on CrCEST measurements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".