Sound Exposure Promotes Intratympanic Drug Delivery to the Inner Ear
Bibliographic record
Abstract
OBJECTIVE: To investigate the impact of sound exposure, with the resultant windows vibration on perilymphatic concentrations following intratympanic (IT) dexamethasone and gentamicin in an animal model. STUDY DESIGN: Animal model blinded study. SETTING: Animal facility of a tertiary medical center. METHODS: Bilateral IT dexamethasone or gentamicin was applied to 15 tested rats. Following injections, each rat was exposed for 3 minutes to free field 30 dB sound pressure level (SPL), 512 vHz noise, with 1 external auditory canal plugged (contralateral control). Following noise exposure, perilymph was obtained from both ears. Drug concentrations were measured using ultrahigh-performance liquid chromatography-mass spectrometer. RESULTS: For dexamethasone, the average (±SD) perilymphatic steroidal concentration was 0.417 µg/mL (±0.549) in the control ears versus 0.487 µg/mL (±0.636) in the sound-exposed ears (P = .008). The average (±SD) gentamicin perilymphatic concentration was 8.628 µg/mL (±2.549) in the sound-exposed ears, compared to 4.930 µg/mL (±0.668) in the contralateral control (nonsound exposed) ears. Sound exposure promoted steroidal and gentamicin diffusion to the inner ear by an averaged (±SD) factor of 1.431 and 1.730 (±0.291 and 0.339), respectively. CONCLUSION: Low-intensity noise (30 dB SPL) was found to enhance dexamethasone phosphate and gentamicin diffusion to the inner ear (by an averaged factor of ∼1.4 and 1.7, respectively) in a murine model.
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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 | 0.000 |
| 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".