Characterization of Tissue-Engineered Posterior Corneas Using Second- and Third-Harmonic Generation Microscopy
Bibliographic record
Abstract
Three-dimensional tissues, such as the cornea, are now being engineered as substitutes for the rehabilitation of vision in patients with blinding corneal diseases. Engineering of tissues for translational purposes requires a non-invasive monitoring to control the quality of the resulting biomaterial. Unfortunately, most current methods still imply invasive steps, such as fixation and staining, to clearly observe the tissue-engineered cornea, a transparent tissue with weak natural contrast. Second- and third-harmonic generation imaging are well known to provide high-contrast, high spatial resolution images of such tissues, by taking advantage of the endogenous contrast agents of the tissue itself. In this article, we imaged tissue-engineered corneal substitutes using both harmonic microscopy and classic histopathology techniques. We demonstrate that second- and third-harmonic imaging can non-invasively provide important information regarding the quality and the integrity of these partial-thickness posterior corneal substitutes (observation of collagen network, fibroblasts and endothelial cells). These two nonlinear imaging modalities offer the new opportunity of monitoring the engineered corneas during the entire process of production.
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 | 0.000 | 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".