Antibody‐Functionalized Gold Nanostar‐Mediated On‐Resonance Picosecond Laser Optoporation for Targeted Delivery of RNA Therapeutics
Bibliographic record
Abstract
The rapid advances of genetic and genomic technology indicate promising therapeutic potential of genetic materials for regulating abnormal gene expressions causing diseases and disorders. However, targeted intracellular delivery of RNA therapeutics still remains a major challenge hindering the clinical translation. In this study, an elaborated plasmonic optoporation approach is proposed to efficiently and selectively transfect specific cells. The site-specific optoporation is obtained by tuning the spectral range of a supercontinuum pulsed picosecond laser in order for each individual cell binding gold nanostar with their unique resonance peak to magnify the local field strength in the near-infrared region and facilitate a selective delivery of small interfering RNA, messenger RNA, and Cas9-ribonucleoprotein into human retinal pigment epithelial cells. Numerical simulations indicate that optoporation is not due to a plasma-mediated process but rather due to a highly localized temperature rise both in time (few nanoseconds) and space (few nanometers). Taking advantage of the numerical simulation and fine-tuning of the optical strategy, the perforated lipid bilayer of targeted cells undergoes a membrane recovery process, important to retain their viability. The results signify the prospects of antibody functionalized nanostar-mediated optoporation as a simple and realistic gene delivery approach for future clinical practices.
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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) | 0.000 | 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".