Bibliographic record
Abstract
Regulating gene expression is an effective way for cells to deal with various stresses. The outcome of this regulation differs with the type of stress, and can promote either cell survival or cell death depending on the severity of the injury incurred. Gene expression can be controlled at several steps, including transcription, translation and degradation. An extensively studied protein involved in translational control is the eukaryotic translation initiation factor 2 (eIF2). When eIF2 becomes phosphorylated on a specific serine residue located within the alpha (alpha) subunit, global protein synthesis is halted. This phosphorylation occurs following periods of environmental stress, and plays a significant role in the cellular response to these events. The eIF2alpha kinase family consists of four members, which are each activated in response to different stimuli. Our group has recently discovered that two members of this family, the protein kinase activated by double-stranded RNA (PKR) and the PKR-like endoplasmic reticulum (ER) kinase (PERK) can also regulate the expression of specific proteins by promoting their degradation by the 26S proteasome. Specifically, we demonstrated that degradation of the cell cycle regulator cyclin D1, and the tumour suppressor p53 was promoted by PERK and PKR during periods of ER stress. This novel function may allow the eIF2alpha kinases to affect a larger number of cellular processes than previously believed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".