Bibliographic record
Abstract
Insulin rapidly activates protein synthesis by activating components of the translational machinery including eIFs (eukaryotic initiation factors) and eEFs (eukaryotic elongation factors). In the long term, insulin also increases the cellular content of ribosomes to augment the capacity for protein synthesis. The rapid activation of protein synthesis by insulin is mediated primarily through phosphoinositide 3-kinase. This involves the activation of PKB (protein kinase B). In one case, PKB acts to phosphorylate and inactivate glycogen synthase kinase 3, which in turn phosphorylates and inhibits eIF2B. Insulin elicits the dephosphorylation and activation of eIF2B. Since eIF2B is required for recycling of eIF2, a factor required for all cytoplasmic translation initiation events, this will contribute to overall activation of protein synthesis. PKB also phosphorylates the TSC1 (tuberous sclerosis complex 1)-TSC2 complex to relieve its inhibitory action on the mTOR (mammalian target of rapamycin). Inhibition of mTOR by rapamycin markedly impairs insulin-activated protein synthesis. mTOR controls translation initiation and elongation. The cap-binding factor eIF4E can be sequestered in inactive complexes by 4E-BP1 (eIF4E-binding protein 1). Insulin elicits phosphorylation of 4E-BP1 and its release from eIF4E, allowing eIF4E to form initiation factor complexes. Insulin induces dephosphorylation and activation of eEF2 to accelerate elongation. Both effects are blocked by rapamycin. Insulin inactivates eEF2 kinase by increasing its phosphorylation at several mTOR-regulated sites. Insulin also stimulates synthesis of ribosomal proteins by promoting recruitment of their mRNAs into polyribosomes. This is inhibited by rapamycin. Several key questions remain about, for example, the mechanisms by which mTOR controls 4E-BP1 and eEF2 kinase and the control of ribosomal protein translation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".