Bibliographic record
Abstract
MicroRNAs (miRNAs) inhibit mRNA expression in general by base pairing to the 3′UTR of target mRNAs and consequently inhibiting translation and/or initiating poly(A) tail deadenylation and mRNA destabilization. We established a mouse Krebs‐2 ascites extract that faithfully recapitulates the miRNA action in cells (Mathonnet et al., 2007). We demonstrated that the let‐7 miRNA inhibits translation of reporter mRNA at the initiation step. Translation inhibition is subsequently consolidated by let‐7‐mediated deadenylation, which requires both the poly(A) binding protein (PABP) and the CAF1 deadenylase, which interact with the let‐7 miRNA‐loaded RNA‐induced silencing complex (miRISC) (Fabian et al., 2009). Importantly, we demonstrated that GW182, a core component of the miRISC, directly interacts with PABP via its C‐terminus and that this interaction enhances miRNA‐mediated deadenylation (Fabian et al., 2009; Jinek et al., 2010). The miRISC binds the deadenylation machinery directly and independently of PABP. We are now studying how the miRISC recruits the deadenylation machinery in a PABP‐independent manner, and how these interactions impact both deadenylation‐dependent and – independent translational repression.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".