Single Pulse Nanosecond Laser‐Stimulated Targeted Delivery of Anti‐Cancer Drugs from Hybrid Lipid Nanoparticles Containing 5 nm Gold Nanoparticles
Bibliographic record
Abstract
Encapsulating chemotherapeutic drugs like doxorubicin (DOX) inside lipid nanoparticles (LNPs) can overcome their acute, systematic toxicity. However, a precise drug release at the tumor microenvironment for improving the maximum tolerated dose and reducing side effects has yet to be well-established by implementing a safe stimuli-responsive strategy. This study proposes an integrated nanoscale perforation to trigger DOX release from hybrid plasmonic multilamellar LNPs composed of 5 nm gold (Au) NPs clustered at the internal lamellae interfaces. To promote site-specific DOX release, a single pulse irradiation strategy is developed by taking advantage of the resonant interaction between nanosecond pulsed laser radiation (527 nm) and the plasmon mode of the hybrid nanocarriers. This approach enlarges the amount of DOX in the target cells up to 11-fold compared to conventional DOX-loaded LNPs, leading to significant cancer cell death. The simulation of the pulsed laser interactions of the hybrid nanocarriers suggests a release mechanism mediated by either explosive vaporization of thin water layers adjacent to AuNP clusters or thermo-mechanical decomposition of overheated lipid layers. This simulation indicates an intact DOX integrity following irradiation since the temperature distribution is highly localized around AuNP clusters and highlights a controlled light-triggered drug delivery system.
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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".