Lubricin: translating an idea into a cure
Bibliographic record
Abstract
Abstract Purpose We hypothesize that lubricin, a boundary lubricant, is transcribed, translated and expressed by the ocular surface and serves to protect the cornea and conjunctiva against significant shear forces generated during an eyelid blink. We also hypothesize that lubricin deficiency increases shear stress and promotes corneal damage. Our objective was to test these hypotheses. Methods Human tissues and cells were processed for molecular, immunochemical and/or biomechanical procedures. Techniques included the use of real‐time PCR, microarrays, sequencing, immunohistochemistry and friction analyses. Methods also involved the corneal evaluation of wildtype and lubricin knockout (KO) mice. Results Our results show that lubricin is produced by human corneal and conjunctival epithelial cells. Lubricin absence in KO mice is associated with a significant increase in corneal fluorescein staining. Our findings also demonstrate that lubricin functions as a very effective friction‐lowering boundary lubricant at the human cornea‐eyelid interface. Conclusion Our data indicate that lubricin acts as a natural boundary lubricant to reduce shear stress at the ocular surface. We thank S Richards, S Liu, A Sahin, R Rahimi Darabad and W Kam (Boston) and S Morrison (Calgary) for their help with these studies. This research was supported by NIH grant R01EY05612, the Margaret S. Sinon Scholar in Ocular Surface Research fund, the Canadian Natural Sciences and Engineering Research Council & the Centre for Bioengineering Research and Education at the University of Calgary. Commercial interest
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".