Anabolic and catabolic mediators of intestinal protein turnover: a new experimental approach
Bibliographic record
Abstract
Studies on regulation of protein turnover in skeletal muscle have revealed the important contributions of protein synthesis and catabolism to tissue protein balance, and have identified a host of specific anabolic and catabolic stimuli and biochemical mechanisms that regulate these processes. This knowledge is critical to current efforts designed to promote anabolism and limit atrophy. Of the tissues with a potentially large contribution to whole-body amino acid metabolism, protein turnover of the intestine stands out as being poorly understood. The intestine is subject to complexities in regulation of its metabolism that are not apparent for other tissues. The study of intestinal protein turnover also entails some important technical challenges. We recently developed an in-situ experimental system for study of intestinal mucosal protein synthesis with the following unique features: multiple observations within an animal; controlled delivery of nutritional stimuli to the apical side, basolateral side, or both; and luminal delivery of tracer in a flooding dose for determination of protein synthesis. We have begun to use the system to test the specific roles of individual luminal nutrients in regulation of mucosal protein synthesis. We have also used protease gene expression as an index of potential regulation of catabolic pathways.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".