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Record W4412125797 · doi:10.1021/acsanm.5c02133

Femtosecond Laser and Gold Nanoparticles-Mediated mRNA Delivery in Corneal Endothelial Cells <i>Ex Vivo</i>

2025· article· en· W4412125797 on OpenAlexafffund
Jennyfer Zapata‐Farfan, Isabelle Brunette, Michel Meunier

Bibliographic record

VenueACS Applied Nano Materials · 2025
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersUniversité de MontréalNanoMedicines Innovation Network
KeywordsEx vivoColloidal goldFemtosecondIn vivoLaserNanoparticleMaterials scienceCell biologyNanotechnologyOpticsBiologyPhysics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.237
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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