Blink detection and magnetic force generation for correction of lagophthalmos, with specific regard to implant compatibility testing
Bibliographic record
Abstract
Purpose The overall goal was to restore a normal and synchronous blink in unilateral lagophthalmos. We describe the biocompatibility profiling of a novel ferromagnetic implant used for electromagnetic eyelid force generation.Methods A non-contact blink detection system and an electromagnetic stimulation system were designed and tested. A modified Lester-Burch speculum equipped with strain gauge technology was used in blinking force measurement. Samarium–cobalt magnets were prototyped and coated with parylene-C. Biocompatibility testing was performed using NIH/3T3 mouse fibroblast cells with MTT colorimetric assay cytotoxic quantification.Outcome Measures Cellular viability and interleukin concentrations.Results Our system was capable of detecting 95.5 ± 3.6% of blinks in various lighting conditions. Using our force measuring device, the difference between non-paralyzed and paralyzed orbicularis oculi (OO) for normal and forceful blinking closure was 40.4 g and 101.9 g, respectively. A 16.6 × 5.0 × 1.5 mm curved shaped samarium cobalt eyelid implant was successfully developed and showed a reproducible blink at 100 ms with full corneal coverage with external eyelid taping. Compared to gold weights, parylene-C coated samarium cobalt implants showed not only excellent cell viability (82.0 ± 4.9% vs. 88.4 ± 0.9%, respectively, p > .05), but also below detection threshold for pro-inflammatory marker concentrations (interleukin-6 < 2 pg/mL and interleukin-10 < 3 pg/mL).Conclusions We demonstrated excellent in-vitro biocompatibility of our parylene-C coated samarium cobalt implants. We believe that our novel approach can improve the quality-of-life of affected individuals and provides new understanding of blinking biomechanics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".