Focused Ultrasound as a Novel Non-Invasive Method for the Delivery of Gold Nanoparticles to Retinal Ganglion Cells
Bibliographic record
Abstract
Purpose: The blood-retinal barrier (BRB) restricts the delivery of intravenous therapeutics to the retina, necessitating innovative approaches for treating retinal disorders. This study sought to explore the potential of focused ultrasound (FUS) to non-invasively deliver intravenously administered gold nanoparticles (AuNPs) across the BRB. FUS-BRB modulation can offer a novel method for targeted retinal therapy. Methods: AuNPs of different sizes and shapes were characterized, and FUS parameters were optimized to permeate the BRB without causing retinal damage in a rodent model. The delivery of 70-kDa dextran and AuNPs to the retinal ganglion cell (RGC) layer was visualized using confocal and two-photon microscopy, respectively. Histological and statistical analyses were conducted to assess the effectiveness and safety of the procedure. Results: FUS-BRB modulation resulted in the delivery of dextran and AuNPs to the RGC and inner nuclear layer. Smaller AuNPs reached the retinal layers to a greater extent than larger ones. The delivery of dextran and AuNPs across the BRB with FUS was achieved without significant retinal damage. Conclusions: This investigation provides the first evidence, to our knowledge, of FUS-mediated AuNP delivery across the BRB, establishing a foundation for a targeted and non-invasive approach to retinal treatment. The results contribute to developing promising non-invasive therapeutic strategies in ophthalmology to treat retinal diseases. Translational Relevance: Modifying the BRB with ultrasound offers a targeted and non-invasive delivery strategy of intravenous therapeutics to the retina.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".