Muscle bioenergetics of speeding fish: In vivo <sup>31</sup>P‐NMR studies in a 4.7 T MR scanner with an integrated swim tunnel
Bibliographic record
Abstract
Abstract Energetic studies on exercising animals are usually limited to oxygen consumption measurements in respirometers followed by invasive tissue sampling and analysis of metabolites. Noninvasive studies of exercising animals like through the use of 31P NMR are typically restricted to “stop and go” measurements. Furthermore, magnetic resonance studies of marine animals are hampered by sea water, a highly electric conductive and dielectric medium, resulting in heavy loading and strong RF loss. In this work, we present a set‐up for online determination of muscle bioenergetics in swimming marine fish, Atlantic cod (Gadus morhua), using in vivo 31P NMR spectroscopy, which overcome these limitations. Special hardware and RF coils were developed for this purpose. A birdcage resonator adapted to high loadings was used for signal excitation. An insulated inductive coil (2 cm diameter) was fixed onto the surface of the fish tail and placed opposite a watertight, passively decoupled 9 × 6 cm2 elliptic and curved surface coil for signal recordings. This arrangement led to enhanced penetration of the RF signal and an almost 10‐fold increase in S/N ratio compared to the exclusive use of the elliptic surface coil. Monitoring of tail beat allowed to set trigger values resulted in an improved quality of in vivo 31P NMR spectra of swimming fish. We report the first successful NMR experiments recording simultaneously tissue energetic and acid–base parameters on swimming cod depending on tail beat frequency and amplitude to determine critical swimming speeds. © 2008 Wiley Periodicals, Inc. Concepts Magn Reson Part B (Magn Reson Engineering) 33B: 62–73, 2008
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".