Glyconeogenic pathway in isolated skeletal muscles of rats
Bibliographic record
Abstract
Although the conversion of lactate to glycogen (glyconeogenesis) in muscle was demonstrated a long time ago, the biochemical reactions responsible for this process are still a controversial matter. In the present study, advantage was taken from the specific inhibition induced by phenylalanine on muscle pyruvate kinase (PK) to investigate the role of reverse PK activity in muscle glyconeogenesis. Addition of phenylalanine to the incubation medium of a preparation of isolated, intact skeletal muscles that maintain metabolic activity for several hours reduced by 50% the rate of incorporation of [14C]lactate or [14C]bicarbonate into muscle glycogen. Muscle extracts presented high levels of maximal activity of PK in the reverse direction, which was completely blocked in the presence of phenylalanine. In contrast, mercaptopicolinic acid, an inhibitor of phosphoenolpyruvate carboxykinase (PEPCK), did not affect the incorporation of 14C from either lactate or bicarbonate into muscle glycogen. Maximal PEPCK activity was much lower in muscle extracts than in gluconeogenic or glyceroneogenic tissues and was suppressed in the presence of mercaptopicolinic acid. The data suggest that a reversal of the metabolic flux through the reaction catalyzed by PK contributes to the accumulation of lactate-derived glycogen that occurs in skeletal muscle under certain physiological conditions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".