3D-Printed Microneedles Create Precise Perforations in Human Round Window Membrane in Situ
Bibliographic record
Abstract
HYPOTHESIS: Three-dimensional (3D)-printed microneedles can create precise holes on the scale of micrometers in the human round window membrane (HRWM). BACKGROUND: An intact round window membrane is a barrier to delivery of therapeutic and diagnostic agents into the inner ear. Microperforation of the guinea pig round window membrane has been shown to overcome this barrier by enhancing diffusion 35-fold. In humans, the challenge is to design a microneedle that can precisely perforate the thicker HRWM without damage. METHODS: Based on the thickness and mechanical properties of the HRWM, two microneedle designs were 3D-printed to perforate the HRWM from fresh frozen temporal bones in situ (n = 18 total perforations), simultaneously measuring force and displacement. Perforations were analyzed using confocal microscopy; microneedles were examined for deformity using scanning electron microscopy. RESULTS: HRWM thickness was determined to be 60.1 ± 14.6 (SD) μm. Microneedles separated the collagen fibers and created slit-shaped perforations with the major axis equal to the microneedle shaft diameter. Microneedles needed to be displaced only minimally after making initial contact with the RWM to create a complete perforation, thus avoiding damage to intracochlear structures. The microneedles were durable and intact after use. CONCLUSION: 3D-printed microneedles can create precise perforations in the HRWM without damaging intracochlear structures. As such, they have many potential applications ranging from aspiration of cochlear fluids using a lumenized needle for diagnosis and creating portals for therapeutic delivery into the inner ear.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".