Therapeutic Potential of Wnt and Notch Signaling and Epigenetic Regulation in Mammalian Sensory Hair Cell Regeneration
Bibliographic record
Abstract
Hearing loss is one of the most prevalent sensory deficits worldwide and can result from the death of mechanosensory hair cells that transduce auditory signals in the cochlea. The mammalian cochlea lacks the capacity to regenerate these hair cells once damaged, and currently there are no biological therapies for hearing loss. Understanding the signaling pathways responsible for hair cell development can inform regenerative strategies and identify targets for treating hearing loss. The canonical Wnt and Notch pathways are critical for cochlear development; they converge on several key molecules, such as Atoh1, to regulate prosensory specification, proliferation, hair cell differentiation, and cellular organization. Much work has focused on Wnt and Notch modulation in the neonatal mouse cochlea, where they can promote hair cell regeneration. However, this regenerative response is limited in the adult cochlea and this might be attributed to age-dependent epigenetic modifications. Indeed, the epigenetic status at key gene loci undergoes dynamic changes during cochlear development, maturation, and aging. Therefore, strategies to improve regenerative success in the adult cochlea might require the modulation of Wnt, Notch, or other pathways, as well as targeted epigenetic modifications to alter the activity of key genes critical for supporting cell proliferation or transdifferentiation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".