Host immunity determines outcome of eukaryotic initiation factor-4A inhibition in breast cancer
Bibliographic record
Abstract
Breast cancer will affect one in eight Canadian women during their lifetime.Furthermore, amongst all breast cancer subtypes, triple-negative breast cancer (TNBC) is the deadliest due to a lack of targeted therapies.One potential way to address this problem is by targeting translation, as abnormal translation of cellular mRNAs in a common feature in many types of cancers, including breast.The RNA helicase eukaryotic translation initiation factor 4A (eIF4A), and the mRNA cap-binding protein eIF4E, are both components of the eIF4F complex.eIF4F is particularly efficient at targeting mRNAs with short half-lives such as Cyclin D1, anti-apoptotic proteins including Bcl-2, and other factors important in tumorigenesis such as TGF-, and VEGF.eIF4A specifically targets mRNA transcripts with complex G/C-rich 5' UTR sequences such as those capable of forming RNA G-quadruplex structures, and it helps to unwind these structured mRNAs to facilitate ribosome scanning.Targeting various components of the eIF4F
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".