The effects of mechanical strain on mouse eye lens capsule and cellular microstructure
Bibliographic record
Abstract
Abstract The understanding of multiscale load transfer within complex soft tissues is incomplete. The eye lens is ideal for multiscale tissue mechanics studies as its principal function is to fine focus light at different distances onto the retina via mechanical shape changes. The biomechanical function, resiliency, and intricate microstructure of the lens make it an excellent non-connective soft tissue model. We hypothesized that compressive strain applied onto whole lens tissue leads to deformation of specific microstructures and that this deformation is reversible following removal of load. For this examination, mouse lenses were compressed by sequential application of increasing load. Using confocal microscopy and quantitative image analysis, we determined that axial strain ≥10% reduces capsule thickness, expands epithelial cell area, and separates fiber cell tips at the anterior region of the lenses. At the equatorial region, strain ≥6% increases fiber cell widths. The effects of strain on lens epithelial cell area, capsule thickness, and equatorial fiber cell widths are reversible following the release of lenses from strain. However, although fiber cell tip separation following the removal of low loads is reversible, the separation becomes irreversible with application of higher loads. This irreversible separation between fiber cell tips leads to incomplete bulk lens resiliency. The lens is an accessible biomechanical model system that provides new insights on multiscale transfer of loads in soft tissues.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".