Pharmaceutical availability of hydrogels with extracts of Arnica montana, Aesculus hippocastanum and Ruscus aculeatus and their potential use as antioxidant polyphenol-rich material
Bibliographic record
Abstract
Introduction Plant extracts are important sources of natural bioactive compounds, commonly used in the pharmaceutical and cosmeceutical industries. Phytochemical and antioxidant screening of plant extracts provides valuable information about their potential use. In the case of polyphenol-rich plant raw materials for external use, it is extremely important to test the ability of active substances to permeate the skin. Aim of the research To determine the polyphenolic content and antioxidant activity of horse chestnut, butcher’s broom, and mountain arnica glycerol-water extracts and to assess the diffusion capacity of plant extracts from hydrogel formulations. Material and methods The content of polyphenols was determined using a spectrophotometric method. Antioxidant properties were analysed using DPPH-EPR assay and the FRAP method. The pharmaceutical availability of the extracts in the hydrogels was tested using a simplified model of human skin represented by a cellulose dialysis membrane. Results and conclusions The total polyphenol content in the extracts was highest in horse chestnut, followed by butcher’s broom and mountain arnica, whereas the arnica extract had the highest flavonoid content. The obtained results indicate that the extracts have high antioxidant potential, which can be ranked as follows: A. montana > A. hippocastanum > R. aculeatus. The pharmaceutical availability analysis showed that the extract from A. hippocastanum is released most efficiently. Analyses of the kinetics of active substance release from hydrogel formulations have proven to be a useful tool for assessing the efficacy of semi-solid products. Tested plant extracts can be used as polyphenol-rich natural materials with antioxidant properties.
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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".