Temporary Versus Permanent Synaptic Loss from Repeated Noise Exposure in Guinea Pigs and C57 Mice
Bibliographic record
Abstract
A single brief noise exposure can cause a significant loss of cochlear afferent synapses without causing permanent threshold shift. Previously we reported that the initial synaptic loss is partially reversible in Guinea pigs, indicating that synaptic loss can be categorized as either temporary or permanent. Since synaptic loss is biased to innervating auditory nerve fibers (ANFs) with low spontaneous spike rates (SSR), which are critical to the coding of in-background noise, coding-in-noise deficits (CIND) have been predicted to result from noise-induced synaptic damage. However, recent study of the noise masking of amplitude-modulation (AM) evoked compound action potentials (CAP) tailed to find evidence for such deficits in either mice or Guinea pigs. The present study sought to determine the effects of repeated noise exposure on temporary and permanent synaptic loss in Guinea pigs and C57 mice, whether such effects were additive, and whether repeated noise exposure induced CIND in Guinea pigs. The results show that the second noise exposure caused much less temporary synaptic loss and no additional permanent loss in Guinea pigs; however, an additional permanent loss was seen after the second noise was in the mice, although it was not significant. In Guinea pigs, the observed increased masking of the AM CAP provides evidence for CIND after repeated noise exposure.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".