Recombinant Thrombomodulin Domain 1 Modulates Macrophage Polarization and Enhances Healing in Corneal Alkali Burns
Bibliographic record
Abstract
Purpose: Corneal alkali burns are severe ocular injuries characterized by intense inflammation, tissue damage, and vision impairment, with current treatments often insufficient in restoring corneal function and clarity. This study aimed to evaluate the therapeutic effects of recombinant thrombomodulin domain 1 (rTMD1) in the treatment of corneal alkali burns, focusing on its impact on inflammation, tissue repair, fibrosis, and neovascularization. Methods: A murine model of corneal alkali burn was utilized to investigate the therapeutic potential of rTMD1. The effects of rTMD1 on macrophage polarization, inflammatory response, tissue repair, fibrosis, and neovascularization were assessed through histological analysis, immunohistochemistry, and molecular studies targeting key signaling pathways such as ERK/HIF-1α and vascular endothelial growth factor (VEGF) expression. Results: Administration of rTMD1 significantly modulated macrophage polarization, promoting a transition from the pro-inflammatory M1 phenotype to the reparative M2 phenotype via inhibition of the ERK/HIF-1α pathway. This shift resulted in reduced inflammation, enhanced tissue repair, and controlled fibrosis. Furthermore, rTMD1 inhibited neovascularization by downregulating VEGF expression, aiding in the preservation of corneal clarity. Conclusions: rTMD1 demonstrates substantial therapeutic potential in treating corneal alkali burns by reducing inflammation, promoting tissue repair, controlling fibrosis, and inhibiting neovascularization. These findings support the further development of rTMD1 as a promising treatment for corneal burns and possibly other inflammatory ocular conditions.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".