In vivo laser targeted gene therapy of retina ganglion cells (Conference Presentation)
Bibliographic record
Abstract
There is a current void in efficient, cell-specific, retinal drug delivery systems, thus developing a safe, effective, selective drug delivery system would open novel therapeutic avenues. We previously demonstrated that femtosecond (fs) laser irradiation can selectively transfect DNA plasmids into cultured cells in the presence of functionalised gold nanoparticles (AuNPs) (1). Here, we sought out to selectively optoporate retinal cells in vivo with functionalized AuNPs and a 800nm fs laser. The cell-surface Kv1.1 voltage-gated channel was chosen to target retinal ganglion cells (RGCs) in the rat retina. The eyes of anesthetized rats were placed in the beam path of an optical system consisting of a fs laser and an ophthalmoscope for fundus visualization. Following Kv1.1-AuNP and FITC-dextran intravitreal injection and incubation, irradiation resulted in FITC uptake by retinal cells. In addition, similar experiments with Cy3-siRNA clearly show that the technique can effectively deliver siRNA into RGCs. Importantly, neither AuNP intravitreal injection nor irradiation resulted in RGC death, as determined by RBPMS quantification 1 week following AuNP injection and/or irradiation. Since living biological tissues absorb energy very weakly at 800nm, this non-invasive tool may provide a safe, cost effective approach to selectively target retinal cells and limit complications associated with surgical interventions, and potential biological hazards associated with viral-based gene therapy. In addition, given the extensive use of lasers in ophthalmic practice, our proposed technology may be seamlessly inserted to current clinical setups. (1) E. Bergeron et al, Nanoscale, 7, 17836 (2015).
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".