Effect of Capsule Thickness and Material Properties on the Central Optical Power of Natural Human Lens
Bibliographic record
Abstract
Background: A thin membrane capsule covers the eye lens and links the lens and the applied forces by the ciliary muscles. The capsule converts the tension of the concentrated zonules to distributed surface tractions over the cortex surface during the lens accommodation. The gradual changes of the capsule geometry and material properties with age and its important role in the design of intraocular lens implants were the motivation of extensive researches on describing the capsule biomechanical behavior. Aim: The present work aimed to study the lens accommodation response to different capsule thicknesses and material properties at different ages. Materials and Methods: A material and geometry parametric study was performed, drawing some guidelines on the choice of lens capsule thickness and biaxial/uniaxial material parameters and exploring the response sensitivity of the finite element model at different age groups Results: The 16-, 35- and 48-year-old lenses were considered for lens accommodation simulation. The sensitivity of lens accommodation was studied at each age group by considering constant thickness (cnst-t) and variable thickness (var-t) capsules and biaxial (Biax) and uniaxial (Uniax) material characteristics tests. The lens was stretched through zonules, and corresponding absolute changes in central optical power (COP) were measured. Conclusion: After the stretch, the lens anterior and posterior curvatures increased, producing a change in COP. The Biax models underestimated the ΔCOP compared to the Uniax models. The 16-year lens model was more sensitive to material properties than thickness variation, while thickness variation was more relevant to the ΔCOP of the 35-year lens model. The 48-year model had the least sensitivity to capsule thickness and material property variations.
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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 | 0.002 |
| 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".