Antioxidant and Anti-Inflammatory Activities of Quercetin 7- <i>O</i> - <b> <i>β</i> </b> -D-Glucopyranoside from the Leaves of <i>Brasenia schreberi</i>
Bibliographic record
Abstract
Brasenia schreberi Gmel. (Cabombaceae) is an aquatic plant that grows in eastern Asia, Australia, Africa, and North and Central America. B. schreberi leaf extracts were obtained by sequential solvent extraction with dichloromethane, methanol, and water. The antioxidant potential of each extract was assessed by using the oxygen radical absorbance capacity (ORAC) assay. With this method, methanol and water extracts were found to be active with mean ± standard deviation values of 7 ± 2 and 5.1 ± 0.5 μmol Trolox® equivalents (TE)/mg, respectively. Two major phenolic compounds, quercetin-7-O-β-D-glucopyranoside and gallic acid, were respectively isolated from the methanolic and water extracts. Both compounds exhibited antioxidant activities, in particular quercetin-7-O-β-D-glucopyranoside (ORAC value, 18 ± 4 μmol TE/μmol). In contrast to its well-known antioxidant homologue quercetin, quercetin-7-O-β-D-glucopyranoside does not inhibit growth of human fibroblasts (WS-1) or murine macrophages (RAW 264.7). Some flavonoids have been reported to possess beneficial effects in cardiovascular and chronic inflammatory diseases associated with overproduction of nitric oxide. Quercetin-7-O-β-D-glucopyranoside possesses anti-inflammatory activity, inhibiting expression of inducible nitric oxide synthase and release of nitric oxide by lipopolysaccharide-stimulated RAW 264.7 macrophages in a dose-dependent manner. Quercetin-7-O-β-D-glucopyranoside also inhibited overexpression of cyclooxygenase-2 and granulocyte macrophage-colony-stimulating factor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".