Biodegradable polymer based theranostic agents for photoacoustic imaging and cancer therapy
Bibliographic record
Abstract
In this study, multifunctional theranostic agents for photoacoustic (PA), ultrasound (US), fluorescent imaging, and for therapeutic drug delivery were developed and tested. These agents consisted of a shell made from a biodegradable Poly(lactide-co-glycolic acid) (PLGA) polymer, loaded with perfluorohexane (PFH) liquid and gold nanoparticles (GNPs) in the core, and lipophilic carbocyanines fluorescent dye DiD and therapeutic drug Paclitaxel (PAC) in the shell. Their multifunctional capacity was investigated in an in vitro study. The PLGA/PFH/DiD-GNPs particles were synthesized by a double emulsion technique. The average PLGA particle diameter was 560 nm, with 50 nm diameter silica-coated gold nano-spheres in the shell. MCF7 human breast cancer cells were incubated with PLGA/PFH/DiDGNPs for 24 hours. Fluorescent and PA images were recorded using a fluorescent/PA microscope using a 1000 MHz transducer and a 532 nm pulsed laser. For the particle vaporization and drug delivery test, MCF7 cells were incubated with the PLGA/PFH-GNPs-PAC or PLGA/PFH-GNPs particles for 6, 12 and 24 hours. The effects of particle vaporization and drug delivery inside the cells were examined by irradiating the cells with a laser fluence of 100 mJ/cm2, and cell viability quantified using the MTT assay. The PA images of MCF7 cells containing PLGA/PFH/DiD-GNPs were spatially coincident with the fluorescent images, and confirmed particle uptake. After exposure to the PLGA/PFHGNP- PAC for 6, 12 and 24 hours, the cell survival rate was 43%, 38%, and 36% respectively compared with the control group, confirming drug delivery and release inside the cells. Upon vaporization, cell viability decreased to 20%. The particles show potential as imaging agents and drug delivery vehicles.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".