Anti-HER2 PLGA-PEG polymer nanoparticle containing gold nanorods and paclitaxel for laser-activated breast cancer detection and therapy
Bibliographic record
Abstract
Phase-transition nanoparticles have been identified as effective theragnostic, anti-cancer agents. However, non-selective delivery of these agents results in inaccurate diagnosis and insufficient treatment. In this study, we report on the development of targeted phase-transition polymeric nanoparticles (NPs) for the imaging and treatment of breast cancer cell lines over-expressing human epidermal growth factor receptor 2 (HER2). These NPs contain a perfluorohexane liquid interior and gold nanorods (GNRs) stabilized by biodegradable and biocompatible copolymer PLGA-PEG. Water-insoluble therapeutic drug Paclitaxel (PAC) and fluorescent dye were encapsulated into the PLGA shell. The NP surfaces were conjugated to HER2-binding agent, Herceptin, to actively target HER2-positive cancer cells. We evaluated the potential of using these NPs as a photoacoustic contrast agent. The efficacy of cancer cell treatment by laser-induced vaporization and stimulated drug release were also investigated. The results showed that our synthesized PLGA-PEG-GNRs (mean diameter 285 ± 29 nm) actively targeted HER2 positive cells with high efficacy. The laser-induced vaporization caused more damage to the targeted cells versus PAC-only and negative controls. This agent may provide better diagnostic imaging and therapeutic potential than current methods for treating HER2-positive breast cancer.
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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".