Unlocking the human inner ear for therapeutic intervention
Bibliographic record
Abstract
The human inner ear contains minute three-dimensional neurosensory structures that are deeply embedded within the skull base, rendering them relatively inaccessible to regenerative therapies for hearing loss. Here we provide a detailed characterisation of the functional architecture of the space that hosts the cell bodies of the auditory nerve to make them safely accessible for the first time for therapeutic intervention. We used synchrotron phase-contrast imaging which offers the required microscopic soft-tissue contrast definition while simultaneously displaying precise bony anatomic detail. Using volume-rendering software we constructed highly accurate 3-dimensional representations of the inner ear. The cell bodies are arranged in a bony helical canal that spirals from the base of the cochlea to its apex; the canal volume is 1.6 μL but with a diffusion potential of 15 μL. Modelling data from 10 temporal bones enabled definition of a safe trajectory for therapeutic access while preserving the cochlea's internal architecture. We validated the approach through surgical simulation, anatomical dissection and micro-radiographic analysis. These findings will facilitate future clinical trials of novel therapeutic interventions to restore hearing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".