Chemical composition and potential antioxidant, anti-inflammatory, and analgesic efficacy of <i>Cistus albidus</i> L.
Bibliographic record
Abstract
Abstract This study aims to assess the chemical composition of the aqueous extract of Cistus albidus L. leaves, as well as the potential of aqueous and hydroethanol extracts of the leaves and seeds as analgesic, anti--inflammatory, and antioxidant agents. The contents of phenolics and inorganic constituents were determined in C. albidus seeds and leaves; antioxidant capacity was assessed by 3 complementary and diverse tests. The carrageenan-induced paw edema technique was used to investigate the anti-inflammatory effect in vivo , and albumin denaturation to evaluate the anti-inflammatory effect in vitro . The acetic acid-induced contortion test, the tail-flick test, and the plantar test were used to assess the analgesic effi cacy in vivo . Chemical analysis was performed by UPLC-MS/MS to quantify several phenolic compounds including catechin (1,627.6 mg kg −1 ), quercitrin (1,235.8 mg kg–1) and gallic acid (628. 2 mg kg −1 ). The ICP analysis revealed that potassium and calcium were the main inorganic components in the seeds and leaves of C. albidus . The hydroethanolic extract of the leaves showed the highest content of polyphenols/flavonoids, whereas the highest value of proantho cyanidins was detected in the aqueous extract of the seeds. All extracts showed potent antioxidant activity related to different phenolic compounds (quercetin, gallic acid, astragalin, catechin, and rutin). The aqueous extract of the leaves strongly inhibited paw edema (76.1 %) after 6 h of treatment and showed maximal inhibition of protein denaturation (191.0 µg mL −1 for 50 % inhibition) and analgesic activity in different nociceptive models. The presented data reveal that C. albidus extracts potentially show antioxidant, anti-inflammatory, and analgesic activities that could confirm the traditional use of this plant.
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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 | 0.001 | 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) | 0.002 | 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".