Microperforations Significantly Enhance Diffusion Across Round Window Membrane
Bibliographic record
Abstract
HYPOTHESIS: Introduction of microperforations in round window membrane (RWM) will allow reliable and predictable intracochlear delivery of pharmaceutical, molecular, or cellular therapeutic agents. BACKGROUND: Reliable delivery of medications into the inner ear remains a formidable challenge. The RWM is an attractive target for intracochlear delivery. However, simple diffusion across intact RWM is limited by what material can be delivered, size of material to be delivered, difficulty with precise dosing, timing, and precision of delivery over time. Further, absence of reliable methods for measuring diffusion across RWM in vitro is a significant experimental impediment. METHODS: A novel model for measuring diffusion across guinea pig RWM, with and without microperforation, was developed and tested: cochleae, sparing the RWM, were embedded in 3D-printed acrylic holders using hybrid dental composite and light cured to adapt the round window niche to 3 ml Franz diffusion cells. Perforations were created with 12.5-μm-diameter needles and examined with light microscopy. Diffusion of 1 mM Rhodamine B across RWM in static diffusion cells was measured via fluorescence microscopy. RESULTS: The diffusion cell apparatus provided reliable and replicable measurements of diffusion across RWM. The permeability of Rhodamine B across intact RWM was 5.1 × 10(9-) m/s. Manual application of microperforation with a 12.5-μm-diameter tip produced an elliptical tear removing 0.22 ± 0.07% of the membrane and was associated with a 35× enhancement in diffusion (P < 0.05). CONCLUSION: Diffusion cells can be applied to the study of RWM permeability in vitro. Microperforation in RWM is an effective means of increasing diffusion across the RWM.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".