<i>In vivo</i> quantification of creatine kinase kinetics in mouse brain using <sup>31</sup> P-MRS at 7 Tesla
Bibliographic record
Abstract
Abstract 31 P-MRS is a method of choice for studying neuroenergetics in vivo , but its application in the mouse brain have been limited, often restricted to ultra-high field (>7 Tesla) MRI scanners. Establishing its feasibility on more readily available preclinical 7 Tesla (T) scanners would create new opportunities to study metabolism and physiology in murine models of brain disorders. Here, we demonstrate that the apparent forward rate constant (k f ) of creatine kinase (CK) can be accurately quantified using a progressive saturation-transfer approach in the mouse brain at 7T. We also find that a reduction of approximately 20% in the breathing rate of anesthetized mice can lead to a 36% increase in k f attributable to a drop in intracellular pH and mitochondrial ATP production. To achieve this, we used a test-retest analysis to assess the reliability and repeatability of 31 P-MRS acquisition, analysis and experimental design protocols. We report that most 31 P-containing metabolites can be reliably measured using a localized 3D-ISIS sequence, which showed highest SNR amplitude, SNR consistency and minimal T 2 relaxation signal loss. Using this protocol, our study identifies, for the first time, key physiological factors influencing mouse brain energy homeostasis in vivo and provides a methodological basis that will guide future studies interested in implementing 31 P-MRS on preclinical 7T scanners.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".