α-amyrin-loaded nanocapsules produce selective cytotoxic activity in leukemic cells
Bibliographic record
Abstract
INTRODUCTION: Amyrins are triterpenes that have attractive pharmacological potential; however, their low water solubility and erratic stomach absorption hinders their use as a drug. The aim of this paper was to develop a novel α-amyrin-loaded nanocapsule for intestinal delivery and evaluate, preliminarily, its cytotoxic ability against leukemic cells. MATERIAL AND METHODS: Five nanocapsule formulations were designed by the solvent displacement-evaporation method. Poly-ε-caprolactone, Eudragit® E100, and Kollicoat® Mae 100 P were used as film-former materials. Particle size, polydispersity index (PdI), zeta potential, and the pH of all formulations were measured. The cytotoxic potential of the nanocapsules was evaluated in vitro using different leukemic lineages RESULTS: Nanocapsules coated with Kollicoat® Mae 100 P presented the smallest particle size (130 nm), the lowest zeta-potential (-38 mV), and the narrowest size distribution (PdI = 0.100). The entrapment efficiency was 65.47%, while the loading capacity was 2.40%. Nanocapsules release 100% of α-amyrin in 40 min (pH 7.4), by using a possible mechanism of swelling-diffusion. The formulation showed excellent on-shelf physicochemical stability during one year. Additionally, nanocapsules produced a selective cytotoxic effect on a human leukemia lineage Kasumi-1, an acute myeloid leukemia cell line, and produced cell death by apoptosis CONCLUSION: α-amyrin-loaded nanocapsules appear to be a promising nanoformulation that could be used against leukemia.
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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".