The Effect of Noise Exposure on Inhibitory Neurotransmission in the Auditory, Visual and Multisensory Cortices in Rats
Bibliographic record
Abstract
It is well‐established that high‐intensity noise exposure can induce structural and physiological changes in the primary auditory cortex, such as impaired GABA neurotransmission which leads to a reduced level of GABA‐synthesizing enzymes (i.e., GAD65/67). At present, however, it remains unknown how partial hearing loss affects GABA neurotransmission in areas of the cortex that process sound as well as other sensory modalities (e.g., visual stimuli). In the present study, we are using a rat model to investigate our working hypothesis that noise‐induced hearing loss causes a differential effect on GAD67 levels in the various cortical areas capable of sound processing, such that the multisensory cortex will not show the same dramatic reduction as in the primary auditory cortex. Adult male rats were exposed to loud noise (0.8‐20 kHz at 120 dB SPL for 2h), which caused a 27 ± 9 dB elevation of their hearing threshold 14 days following the noise exposure. Using immunohistochemistry, the level of GAD67 was compared between noise‐exposed rats and age‐matched controls in multiple auditory, visual and multisensory cortical areas. Consistent with our hypothesis, preliminary findings suggest that the multisensory cortex does not experience the same degree of impaired GABA neurotransmission as the primary auditory cortex following noise‐induced hearing loss.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".