Examining the expression of potential cell-cycle regulators in the developing mouse inner ear
Bibliographic record
Abstract
Sensory epithelia of the mammalian inner ear are composed of hair cells and supporting cells. These cells originate from common precursors, which exit the cell cycle during late embryogenesis. Thereafter, hair cells and supporting cells maintain a non-proliferative state. As a result, hair cell loss in mammals is irreversible. If inner ear organs are to be regenerated, supporting cells must reenter the cell cycle to produce new hair cells and supporting cells. Little is known about genes that maintain the post-mitotic state of inner ear supporting cells. Previous studies revealed the role of cell cycle inhibitors such as p27kip in regulating the post-mitotic fate of differentiated supporting cells (Lowenheim H, Furness DN et al. 1999) The purpose of our study was to identify other potential regulators of cell cycle in supporting cells and determine their expression at different ages. We used the Shared Harvard Inner-Ear Laboratory data base (SHIELD; shield.hms.harvard.edu) to select 15 candidate genes following these criteria: 1) an established role in cell cycle regulation in other cell types, 2) maintained postnatal expression in mouse utricular supporting cells, and 3) not previously studied in the mouse inner ear.We used RT-PCR and in situ hybridization to determine the expression of these candidate genes in inner ear sensory epithelia. Our RT-PCR results, from mouse utricles between postnatal days 7-8, (which contained hair cells, supporting cells, and non-sensory cells) confirmed the expression of all candidate genes. Our in situ hybridization results, which came from mice at embryonic days 13.5, E15.5 and 18.5, were mixed. In some cases expression was seen mainly in supporting cells, as expected based on the SHIELD data and in other cases in hair cells or non-sensory cells. Further studies are required to test whether these genes play a role in regulating cell cycle in the mouse developing inner ear.
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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 | 0.001 | 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) | 0.001 | 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".