Development of propolis nanoparticles for the treatment of bovine mastitis: in vitro studies on antimicrobial and cytotoxic activities
Bibliographic record
Abstract
This study describes the development of propolis nanoparticles (PNP) to treat bovine mastitis. Three PNP prepared with varying concentrations of propolis (5% and 7%, w/v) and the surfactants [poloxamer (1%, 3%, and 4%, w/v) and soy lecithin (0.25%, 0.7%, and 1%, w/v)]. PNP were characterized according to their size, polydispersity, zeta potential, pH, morphology, and physical stability. PNP were evaluated for their in vitro antimicrobial and cytotoxic effects. PNP obtained were spherical with a monodisperse distribution (polydispersity index < 0.2) and an average particle size between 181 and 201 nm. Stability studies showed that PNP were stable over 150 d. The encapsulation efficiency of total phenolic content varied between 73% and 91%. The chromatographic profile of phenolic compounds from PNP showed selective encapsulation efficiency according to the polarity of compounds. All PNP showed antimicrobial activity against Staphylococcus aureus with a minimum inhibitory concentration ranging from 156 to 310 μg mL−1. The IC50 (the concentration responsible for reduction of cellular viability by half) for epithelial cells of bovine mammary gland (MAC-T, mammary alveolar cell-T) varied from 122.2 to 268.4 μg mL−1. Results showed that PNP represent a promising nanocarrier for high concentrations of propolis extract in a stable aqueous medium, while, at the same time, presenting antimicrobial activity accompanied by moderate cytotoxicity to the MAC-T cells.
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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".