Phenolic Extract from Aralia nudicaulis L. Rhizomes Inhibits Cellular Oxidative Stresses
Bibliographic record
Abstract
UV-B and IR-A radiation are important inducers of biological changes in skin involving ROS generation. The overloading of antioxidant defense mechanisms by ROS production could lead to photoaging and photocarcinogenesis processes. Various traditional usages are reported for Aralia nudicaulis L. extracts, including treatment of dermatological disorders. Antioxidant and anti-inflammatory properties have already been reported for other Aralia species possibly due to the presence of phenolic compounds. However, the phenolic composition and the potential activity of A. nudicaulis rhizomes extract against oxidative stress and UV/IR damages have not been investigated. The main aims of this study were to prepare a fraction enriched in phenolic compounds (FEPC) from A. nudicaulis rhizomes, to identify its major phenolic compounds and to assess its potential for protective effects against oxidative stress induced by UV-B, IR-A or inflammation. A quantitative LC-MS study of FEPC shows that chlorogenic, caffeic and protocatechuic acids are the main phenolic compounds present, with concentrations of 15.6%, 15.3% and 4.8% of the total composition, respectively. With a validated analytical method, those compounds were quantified over different stages of the growing period. As for biological potential, first this extract demonstrates antioxidant and anti-inflammatory activities. Furthermore, ROS generation induced by IR-A and UV-B were strongly inhibited by A. nudicaulis extract, suggesting that Aralia nudicaulis L. rhizome extract could protect dermal cells against oxidative stress induced by UV-B and IR-A.
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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".