Analysis of Translation Initiation During Stress Conditions by Polysome Profiling
Bibliographic record
Abstract
Precise control of mRNA translation is fundamental for eukaryotic cell homeostasis, particularly in response to physiological and pathological stress. Alterations of this program can lead to the growth of damaged cells, a hallmark of cancer development, or to premature cell death such as seen in neurodegenerative diseases. Much of what is known concerning the molecular basis for translational control has been obtained from polysome analysis using a density gradient fractionation system. This technique relies on ultracentrifugation of cytoplasmic extracts on a linear sucrose gradient. Once the spin is completed, the system allows fractionation and quantification of centrifuged zones corresponding to different translating ribosomes populations, thus resulting in a polysome profile. Changes in the polysome profile are indicative of changes or defects in translation initiation that occur in response to various types of stress. This technique also allows to assess the role of specific proteins on translation initiation, and to measure translational activity of specific mRNAs. Here we describe our protocol to perform polysome profiles in order to assess translation initiation of eukaryotic cells and tissues under either normal or stress growth 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".