Anti-HER2 PLGA-PEG polymer nanoparticle containing gold nanorods and paclitaxel for laser-activated breast cancer detection and therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it