Near Infra-Red Labelling and Tracking of Transplanted Corneal Endothelial Graft
Bibliographic record
Abstract
Abstract Following corneal transplantation, there is an initial decline in corneal endothelial cells (CECs) following graft preparation and surgery. Monitoring post-transplantation is only possible months after surgery by specular microscopy, which has a limited field of view. We have developed a labelling approach using 1,1’-dioctadecyl-3,3,3’,3’-tetramethylindotricarbocyanine iodide (DIR) dye solution, that enabled tracking of labelled CECs in vivo for at least one month. Initial in vitro optimization of dye concentration, cellular toxicity and real-time cell migration was assessed using propagated primary CECs. Subsequent in vivo evaluation of cellular labelling was assessed within a rabbit wound healing model. Finally, real-time visualization of human cadaver donor tissue incubated in DIR transplanted into rabbits was achieved using the Heidelberg Spectralis. Results revealed detectable fluorescence increased with concentration to a plateau of 100µg/ml, with no toxicity of CECs at any concentration evaluated. DIR-labelled CECs were detectable in vivo upto 1 month, and transplanted labelled donor graft could be visualized and were trackable in vivo. Acute endothelial rejection in 1 rabbit was evidenced by detectable DIR positive cells within the anterior chamber. DIR imaging allowed for detailed imaging of the transplanted corneal endothelium, and enabled non-invasive observation of the corneal endothelial morphology following transplantation.
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".