Characteristics and Antioxidant Activities of Silymarin Nanoparticles
Bibliographic record
Abstract
Silymarin, a well known hepatoprotective drug, has been routinely used in treating liver disorders. However, its bioavailability and therapeutic efficiency are limited by the poor aqueous solubility. In this study, we used the nanoprecipitation technique to develop a nanoparticles system to improve the solubility of silymarin. The newly developed silymarin nanoparticles were characterized for mean particle size, morphology, intermolecular interaction, crystalline features and dissolution property, as well as assessing for antioxidant activities. Results indicated that a drastic change in the physiochemical properties of silymarin was noted in the form of nanoparticles, as displayed by the extremely small particle size (46.1 +/- 1.73 nm), the formation of intermolecular hydrogen bonding between silymarin and matrix of nanoparticles, and the rendering of amorphous state. These phenomena have contributed to the enhanced dissolution property of silymarin nanoparticles, as well as a greater potency in DPPH radical scavenging, anti-superoxide anion formation, and superoxide anion scavenging activities than the crude silymarin. The present study concludes that silymarin nanoparticles have an improved physicochemical property as demonstrated by an increased solubility and enhanced antioxidant activities.
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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".