Isoflurane titration improves detection of hippocampal lactate by 1H-MRS
Bibliographic record
Abstract
Abstract Lactate has increasingly been recognized as both an important fuel source and a signaling molecule within the brain. Alterations in brain lactate levels are associated with various neurological diseases. Thus, there is great interest in the in vivo detection and measurement of cerebral lactate levels in animals used for investigation of normal brain function and models of disease. Proton magnetic resonance spectroscopy (1H-MRS) is a non-invasive technique used to measure lactate and other metabolites within the brain. However, lactate can be difficult to detect with conventional 1H-MRS due to its low abundance and spectral overlap with lipids. In addition, volatile anesthetics used during image acquisition increase lactate production, potentially masking any subtle physiological changes in lactate levels. Here, we made use of a transgenic mouse model in which expression of lactate dehydrogenase A (Ldha), the rate-limiting enzyme of lactate production, was induced within cortical and hippocampal neurons. Unexpectedly, 1H-MRS analysis, under typical isoflurane-induced anesthesia of 4% induction followed by 1.6–2% maintenance, revealed no significant elevation of hippocampal lactate levels in neuronal Ldha induction mice compared to control mice. In contrast, 1H-MRS analysis, using an isoflurane titration protocol in which mice were sequentially exposed to 1.6%, 2%, and then finally 3% isoflurane, revealed significantly higher hippocampal lactate levels in Ldha transgenic mice compared to controls. In addition, significantly fewer mice were required to detect differences in lactate levels using the isoflurane titration protocol compared to conventional isoflurane-induced anesthesia. Our findings highlight the importance of controlling for the effects of anesthesia when detecting changes in hippocampal lactate levels in vivo and offer a novel protocol for enhanced cerebral lactate detection.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".