Vaporization, photoacoustic and acoustic characterization of PLGA/PFH particles loaded with optically absorbing materials
Bibliographic record
Abstract
Poly (lactide-co-glycolic acid) (PLGA) is an FDA approved biocompatible and biodegradable material that is commonly used in implantation and drug delivery applications. It can be used as a carrier for various chemotherapeutic drugs, imaging agents and targeting moieties. Perfluorocarbon (PFC) liquids have been used in various biomedical applications, and can be activated (i.e. the liquid core converted to gas) via laser irradiation through the incorporation of optically absorbing nanoparticles or dyes within the emulsions. PLGA particles were synthesized with nanoparticles (gold or iron oxide) or dyes (DiI or rhodamine) within the PLGA shell and perfluorohexane (PFH) in the core. The photoacoustic signals and vaporization threshold of individual micron-sized particles were examined to optimize the dye and nanoparticle combination. The PLGA/PFH particles containing gold nanoparticles and DiI had the lowest vaporization threshold, with each particle consistently requiring less than 100 mJ/cm2for vaporization. The effects of vaporization were then tested in cell culture. The particles were internalized by MDA breast cancer cells, and then irradiated with a laser. Once the particle was vaporized, a bubble formed within the cell which destroyed the cell. This work presents a first study of using a solid-shell PLGA particle encapsulating PFH liquid as a theragnostic agent.
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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".