Insulin-like Factor 3 Promotes Wound Healing at the Ocular Surface
Bibliographic record
Abstract
Tear fluid is known to contain many different hormones with relevance for ocular surface homeostasis. We studied the presence and functional role of insulin-like factor 3 (INSL3) and its cognate receptor RXFP2 (relaxin/insulin-like family peptide receptor 2) at the ocular surface and in tears. Expression of human INSL3 and RXFP2 was determined in tissues of the ocular surface and lacrimal apparatus; in human corneal (HCE), conjunctival (HCjE), and sebaceous (SC) epithelial cell lines; and in human tears by RT-PCR and ELISA. We investigated effects of human recombinant INSL3 (hrINSL3) on cell proliferation and cell migration and the influence of hrINSL3 on the expression of MMP2, -9, and -13 and TIMP1 and -2 was quantified by real-time PCR and ELISA in HCE, HCjE, and SC cells. We used a C57BL/6 mouse corneal defect model to elucidate the effect of topical application of hrINSL3 on corneal wound healing. INSL3 and RXFP2 transcripts and INSL3 protein were detected in all tissues and cell lines investigated. Significantly higher concentrations of INSL3 were detected in tears from male vs. female volunteers. Stimulation of HCE, HCjE, and SC with hrINSL3 significantly increased cell proliferation in HCjE and SC and migration of HCjE. Treatment with hrINSL3 for 24 hours regulated MMP2, TIMP1, and TIMP2 expression. The local application of hrINSL3 onto denuded corneal surface resulted in significantly accelerated corneal wound healing in mice. These findings suggest a novel and gender-specific role for INSL3 and cognate receptor RXFP2 signaling in ocular surface homeostasis and determined a novel role for hrINSL3 in corneal wound healing.
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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".