Bibliographic record
Abstract
The cornea is critical for vision, and corneal healing after trauma is fundamental in maintaining its transparency and function. Through the study of corneal injury models, researchers aim to enhance their understanding of how the cornea heals and develop strategies to prevent and manage corneal opacities. Chemical injury is one of the most popular injury models that has extensively been studied on mice. Most previous investigators have used a flat paper soaked in sodium hydroxide to induce corneal injury. However, inducing corneal and limbal injury using flat filter paper is unreliable, since the mouse cornea is highly curved. Here, we present a new instrument, a modified biopsy punch, that enables the researchers to create a well-circumscribed, localized, and evenly distributed alkali injury to the murine cornea and limbus. This punch-trephine method enables researchers to induce an accurate and reproducible chemical burn to the entire murine cornea and limbus while leaving other structures, such as the eyelids, unaffected by the chemical. Moreover, this study introduces an enucleation technique that preserves the medial caruncle as a landmark for identifying the nasal side of the globe. The bulbar and palpebral conjunctiva, and lacrimal gland are also kept intact using this technique. Ophthalmologic examinations were performed via slit lamp biomicroscope and fluorescein staining on days 0, 1, 2, 6, 8, and 14 post-injury. Clinical, histological, and immunohistochemical findings confirmed limbal stem cell deficiency and ocular surface regeneration failure in all experimental mice. The presented alkali corneal injury model is ideal for studying limbal stem cell deficiency, corneal inflammation, and fibrosis. This method is also suitable for investigating pre-clinical and clinical efficacies of topical ophthalmologic medications on the murine corneal surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.587 | 0.352 |
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".