Femtosecond Laser and Gold Nanoparticles-Mediated mRNA Delivery in Corneal Endothelial Cells <i>Ex Vivo</i>
Bibliographic record
Abstract
Endothelial diseases impair the vision of approximately 246 million people worldwide. The only approved treatment is corneal transplantation, which is invasive, costly, and limited by donor shortages. We present a femtosecond (fs) laser-based method for gene delivery to corneal endothelial cells (CECs) via transcorneal irradiation of gold nanoparticles (AuNPs). Upon irradiation, the plasmonic response of AuNPs transiently permeabilizes CEC membranes (optoporation), enabling the delivery of exogenous molecules such as mRNA. Mouse and rabbit corneas are used ex vivo to evaluate mRNA transfection and nanoparticle uptake, respectively. Mice receive intracameral injections of AuNPs and Cy3 EGFP mRNA, while rabbit corneas are incubated with AuNPs and fluorophores. After 1 h incubation, a 45 fs laser (λ = 800 nm, 1 kHz) is focused on the CEC layer. Fluorescence and bright-field microscopy assess optoporation, viability, and specificity. Lipofectamine-mRNA serves as a transfection control. Results show successful optoporation, with CEC viability above 70% at 48 h, 49% mRNA transfection in mice, and 95% fluorophore uptake in rabbits. This targeted, noninvasive approach offers a promising alternative for gene and drug delivery to CECs.
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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".