Suppression of eukaryotic initiation factor 4E prevents chemotherapy-induced alopecia
Bibliographic record
Abstract
BACKGROUND: Chemotherapy-induced hair loss (alopecia) (CIA) is one of the most feared side effects of chemotherapy among cancer patients. There is currently no pharmacological approach to minimize CIA, although one strategy that has been proposed involves protecting normal cells from chemotherapy by transiently inducing cell cycle arrest. Proof-of-concept for this approach, known as cyclotherapy, has been demonstrated in cell culture settings. METHODS: The eukaryotic initiation factor (eIF) 4E is a cap binding protein that stimulates ribosome recruitment to mRNA templates during the initiation phase of translation. Suppression of eIF4E is known to induce cell cycle arrest. Using a novel inducible and reversible transgenic mouse model that enables RNAi-mediated suppression of eIF4E in vivo, we assessed the consequences of temporal eIF4E suppression on CIA. RESULTS: Our results demonstrate that transient inhibition of eIF4E protects against cyclophosphamide-induced alopecia at the organismal level. At the cellular level, this protection is associated with an accumulation of cells in G1, reduced apoptotic indices, and was phenocopied using small molecule inhibitors targeting the process of translation initiation. CONCLUSIONS: Our data provide a rationale for exploring suppression of translation initiation as an approach to prevent or minimize cyclophosphamide-induced alopecia.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".