Delivery of Apoptosis-inducing Piperine to Triple-negative Breast Cancer Cells <i>via</i> Co-polymeric Nanoparticles
Bibliographic record
Abstract
BACKGROUND/AIM: Piperine, a major alkaloid of the fruit of black pepper plants, selectively inhibits the growth of triple-negative breast cancer cells but its lipophilicity restricts possible clinical application. This study therefore determined the feasibility of encapsulating piperine in nanoparticles (NPs) to increase its solubility in an aqueous environment. MATERIALS AND METHODS: Piperine-loaded biodegradable methoxy poly(ethylene glycol)-poly(lactic-co-glycolic) acid copolymer-based NPs were produced by single emulsion solvent extraction and thin-film hydration. Growth and viability of triple-negative breast cancer (TNBC) cells were determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and Annexin-V-FLUOS/propidium iodide staining, respectively. RESULTS: Thin-film hydration was superior to single emulsion solvent extraction, yielding piperine-loaded NPs with an average size of 50 nm. Piperine-loaded NPs inhibited TNBC cell growth and induced apoptosis while sparing normal fibroblasts. CONCLUSION: It is feasible to deliver a cytotoxic concentration of piperine to TNBC cells via NPs with the potential for improved bioavailability and solubility in biological fluids.
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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".