Metabolic protein replacement drives tissue turnover in adult mice
Bibliographic record
Abstract
Stable isotopes are increasingly being used to examine ecological and physiological questions, such as dietary choices, migration routes and timing, and physiological condition. To address these questions in the field, laboratory experiments must be done to determine diet–tissue discrimination values and turnover rates for stable isotopes in tissues. In this study, we examined the carbon and nitrogen turnover rates of whole blood, skeletal muscle, liver, kidney, heart, and brain, as well as the sulfur turnover rate of whole blood, skeletal muscle, and liver in Mus musculus L., 1758 following a diet change. By examining tissue isotope change in two groups of mice fed different diets, we found that tissues turnover at different rates (in order of fastest to slowest — liver, kidney, heart, brain, whole blood, skeletal muscle), but that carbon, nitrogen, and sulfur isotopes turned over with similar half-lives within a single tissue. By using a diet with different nutrient isotopic values, we also calculated that up to approximately 90%–95% of carbon in newly synthesized tissue was contributed by dietary protein. These results will provide field researchers with additional tissue isotopic half-lives to elucidate dietary history with a greater degree of certainty. The tissue sulfur half-lives provide an extra stable isotope that may be used in situations where carbon and nitrogen values do not differ between old and new nutrient sources.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".