Differential effects of pannexins on noise-induced hearing loss
Bibliographic record
Abstract
Hearing loss, including noise-induced hearing loss, is highly prevalent and severely hinders an individual's quality of life, yet many of the mechanisms that cause hearing loss are unknown. The pannexin (Panx) channel proteins, Panx1 and Panx3, are regionally expressed in many cell types along the auditory pathway, and mice lacking Panx1 in specific cells of the inner ear exhibit hearing loss, suggesting a vital role for Panxs in hearing. We proposed that Panx1 and/or Panx3 null mice would exhibit severe hearing loss and increased susceptibility to noise-induced hearing loss. Using the auditory brainstem response, we surprisingly found that Panx1−/− and Panx3−/− mice did not harbor hearing or cochlear nerve deficits. Furthermore, while Panx1−/− mice displayed no protection against loud noise-induced hearing loss, Panx3−/− mice exhibited enhanced 16- and 24-kHz hearing recovery 7 days after a loud noise exposure (NE; 12 kHz tone, 115 dB sound pressure level, 1 h). Interestingly, Cx26, Cx30, Cx43, and Panx2 were up-regulated in Panx3−/− mice compared with wild-type and/or Panx1−/− mice, and assessment of the auditory tract revealed morphological changes in the middle ear bones of Panx3−/− mice. It is unclear if these changes alone are sufficient to provide protection against loud noise-induced hearing loss. Contrary to what we expected, these data suggest that Panx1 and Panx3 are not essential for baseline hearing in mice tested, but the therapeutic targeting of Panx3 may prove protective against mid-high-frequency hearing loss caused by loud NE.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".