Noise-induced damage to ribbon synapses without permanent threshold shifts in neonatal mice
Bibliographic record
Abstract
Recently, ribbon synapses to the hair cells (HCs) in the cochlea have become a novel site of interest in the investigation of noise-induced cochlear lesions in adult rodents (Kujawa and Liberman, 2009; Lin et al., 2011; Liu et al., 2012; Shi et al., 2013). Permanent noise-induced damage to this type of synapse can result in subsequent degeneration of spiral ganglion neurons (SGNs) in the absence of permanent changes to hearing sensitivity. To verify whether noise exposure during an early developmental period produces a similar impact on ribbon synapses, the present study examined the damaging effects of noise exposure in neonatal Kunming mice. The animals received exposure to broadband noise at 105-decibel (dB) sound pressure level (SPL) for 2h on either postnatal day 10 (P10d) or postnatal day 14 (P14d), and then hearing function (based on the auditory brainstem response (ABR)) and cochlear morphology were evaluated during either postnatal weeks 3-4 (P4w) or postnatal weeks 7-8 (P8w). There were no significant differences in the hearing threshold between noise-exposed and control animals, which suggests that noise did not cause permanent loss of hearing sensitivity. However, noise exposure did produce a significant loss of ribbon synapses, particularly in P14d mice, which continued to increase from P4w to P8w. Additionally, a corresponding reduction in the amplitude of compound action potential (CAP) was observed in the noise-exposed groups at P4w and P8w, and the CAP latency was elongated, indicating a change in synaptic function.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| 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".