Preparation of psoralen polymer–lipid hybrid nanoparticles and their reversal of multidrug resistance in MCF-7/ADR cells
Bibliographic record
Abstract
Multidrug resistance (MDR) is the leading cause of failure for breast cancer in the clinic. Thus far, polymer-lipid hybrid nanoparticles (PLN) loaded chemotherapeutic agents has been used to overcome MDR in breast cancer. In this study, we prepared psoralen polymer-lipid hybrid nanoparticles (PSO-PLN) to reverse drug resistant MCF-7/ADR cells in vitro and in vivo. PSO-PLN was prepared by the emulsification evaporation-low temperature solidification method. The formulation, water solubility and bioavailability, particle size, zeta potential and entrapment efficiency, and in vitro release experiments were optimized in order to improve the activity of PSO to reverse MDR. Optimal formulation: soybean phospholipids 50 mg, poly(lactic-co-glycolic) acid (PLGA) 15 mg, PSO 3 mg, and Tween-80 1%. The PSO-PLN possessed a round appearance, uniform size, exhibited no adhesion. The average particle size was 93.59 ± 2.87 nm, the dispersion co-efficient was 0.249 ± 0.06, the zeta potential was 25.47 ± 2.84 mV. In vitro analyses revealed that PSO resistance index was 3.2, and PSO-PLN resistance index was 5.6, indicating that PSO-PLN versus MCF-7/ADR reversal effect was significant. Moreover, PSO-PLN is somewhat targeted to the liver, and has an antitumor effect in the xenograft model of drug-resistant MCF-7/ADR cells. In conclusion, PSO-PLN not only reverses MDR but also improves therapeutic efficiency by enhancing sustained release of PSO.
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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) | 0.000 | 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 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".