New Insights on Acanthus ebracteatus Vahl: UPLC-ESI-QTOF-MS Profile, Antioxidant, Antimicrobial and Anticancer Activities
Bibliographic record
Abstract
This study investigated the antioxidant, antimicrobial, anticancer, and phytochemical profiling of extracts from the leaves and stem/root of Acanthus ebracteatus (AE). The total phenolic content (TPC), total flavonoid content (TFC), 2,2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) radical-scavenging activity, 2, 2′-azino-Bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical-scavenging activity, metal chelating activities (MCA), ferric reducing antioxidant power (FRAP) and oxygen radical antioxidant capacity (ORAC) were used for antioxidant assessment. The ethanolic extracts of the leaves (AEL-nor) and stem/root (AEWP-nor) without chlorophyll removal and those with chlorophyll removal, using sedimentation process (AEL-sed and AEWP-sed), were prepared. Generally, AEL-sed showed the highest antioxidant activity (FRAP: 1113.2 µmol TE/g; ORAC: 11.52 µmol TE/g; MCA: 47.83 µmol EDTA/g; ABTS 67.73 µmol TE/g; DPPH 498.8 µmol TE/g; TPC: 140.50 mg/GAE g and TFC: 110.40 mg/CE g) compared with other extracts. Likewise, AEL-sed also showed the highest bacteriostatic (MIC) and bactericidal (MBC) effects, as well as the highest anticancer and antiproliferative activity against oral squamous carcinoma (CLS-354/WT) cells. UPLC-ESI-QTOF/MS analysis of AEL-sed and AEWP-sed tentatively identified several bioactive compounds in the extracts, including flavonoids, phenols, iridoids, and nucleosides. Our results provide a potentially valuable application for A. ebracteatus, especially in further exploration of the plant in oxidative stress-related disorders, as well as the application of the plant as potential nutraceuticals and cosmeceuticals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".