Clitoria ternatea L. petal bioactive compounds display antioxidant, antihemolytic and antihypertensive effects, inhibit α-amylase and α-glucosidase activities and reduce human LDL cholesterol and DNA induced oxidation
Bibliographic record
Abstract
The purpose of this study was to use a statistical approach to optimise the experimental conditions regarding the extraction of bioactive compounds, and to analyse the in vitro functional properties of crude lyophilized extracts (CLE) and partially purified (PPE) extracts of Clitoria ternatea petals. The results showed that the factors of temperature and time influenced the extraction of phenolic compounds, antioxidant activity and the physicochemical parameters. Simultaneous optimisation showed that the same levels of bioactive compounds were extracted when using temperatures from 11.7 to 68.3 °C and times from 8.47 to 51.12 min. Principal component analysis revealed the experimental conditions that provided the extraction producing the highest level of phenolic content (40 °C/30 min). The CLE showed antimicrobial activity; protective effect against hemolysis of erythrocytes; inhibition of α-amylase, α-glucosidase and angiotensin-I-converting (ACE-I) enzymes; and inhibition of lipid peroxidation. The CLE and PPE demonstrated oxygen radical absorption capacity; inhibition of DNA strand scission; inhibition of LDL cholesterol oxidation; intracellular antioxidant activity against reactive oxygen species (>100 μg/mL); and no cytotoxicity (IC50, GI50 and LC50 > 900 μg/mL) against A549, HCT8 and IMR90 cell lines.
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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) | 0.001 | 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) | 0.002 | 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".