Commentary: Tracing the fate of metabolic substrates during changes in whole-body energy expenditure in mice
Bibliographic record
Abstract
For small mammals, such as mice, cannulation procedures can be quite challenging, limiting research associated with tracing isotopically labelled substrates at the whole-animal level. When cannulation in mice is possible, assessment of substrate use is further limited to when mice are either under anesthesia or are at rest, as there are no studies directly quantifying substrate use during exercise in mice. The use of isotopic tracer techniques has greatly advanced our knowledge in understanding how metabolic substrates (carbohydrates, amino acids, and fatty acids) contribute to whole-body metabolism. However, research regarding tissue-specific fuel use contributions to whole-body energy expenditure in mice at varying metabolic intensities (i.e., exercise) is lacking, despite the popularity of using mice in a variety of metabolic models. In this commentary, we briefly discuss the methodologies, advantages, and disadvantages of using radiolabelled, positron emission, and stable isotopes with a specific focus on fatty acids. We highlight recent mouse studies that have used creative experimental designs employing the use of isotopic tracer techniques and we briefly discuss how these methodologies can be further pursued to deepen our understanding of substrate use during exercise. Lastly, we show findings of a recent study we performed using a radiolabelled fatty acid tracer (14C-bromopalmitic acid) to determine fatty acid uptake in 16 muscles, two brown and two white adipose tissue depots during submaximal exercise in deer mice.
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 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.026 | 0.030 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".