Signalling to translation: how signal transduction pathways control the protein synthetic machinery
Bibliographic record
Abstract
Recent advances in our understanding of both the regulation of components of the translational machinery and the upstream signalling pathways that modulate them have provided important new insights into the mechanisms by which hormones, growth factors, nutrients and cellular energy status control protein synthesis in mammalian cells. The importance of proper control of mRNA translation is strikingly illustrated by the fact that defects in this process or its control are implicated in a number of disease states, such as cancer, tissue hypertrophy and neurodegeneration. Signalling pathways such as those involving mTOR (mammalian target of rapamycin) and mitogen-activated protein kinases modulate the phosphorylation of translation factors, the activities of the protein kinases that act upon them and the association of RNA-binding proteins with specific mRNAs. These effects contribute both to the overall control of protein synthesis (which is linked to cell growth) and to the modulation of the translation or stability of specific mRNAs. However, important questions remain about both the contributions of individual regulatory events to the control of general protein synthesis and the mechanisms by which the translation of specific mRNAs is controlled.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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".