Cytotoxic, anti-inflammatory and antioxidant activities of four different extracts of <i>Galega officinalis</i> L (Goat’s rue)
Bibliographic record
Abstract
Purpose: To evaluate the cytotoxic, anti-inflammatory and antioxidant activities of four different solvent extracts obtained from the aerial parts of Galega officinalis L.Methods: The hexane, DCM, methanol and water extracts of G. officinalis were successively obtained by soxhlet extraction method. The cytotoxic activity of the extracts was assessed against human lung carcinoma (A-549), human colorectal adenocarcinoma (HT-29), human brain glioblastoma (U-87), and colon adenocarcinoma (DLD-1) by Resazurine test. The antioxidant activity of extracts were determined by Folin-Ciocalteau, oxygen radical absorbing capacity (ORAC), and 2’.7’-dichlorofluorescin-diacetate (DCFH-DA) cell-based assay while their anti-inflammatory activity was determined by nitric oxide (NO) assay.Results: DCM extract showed strong cytotoxic activity against lung adenocarcinoma and brain glioblastoma cell lines, with IC50 (concentration inhibiting 50 % of cell growth) values of 11 ± 0.4 and 16 ± 3 μg/mL, respectively. The hexane extract showed moderate anticancer activity against the same cell lines (59 ± 13 and 63 ± 16 μg/mL, respectively). DCM extract also showed significant anti-inflammatory activity, inhibiting NO release by 86.7 % at 40 μg/mL in lipopolysaccharide (LPS) - stimulated murine RAW 264.7 macrophages. Of all test extracts, the methanol extract of G. officinalis showed the highest antioxidant activity with 2.33 ± 0.09 μmol Trolox/mg , 7.10 ± 0.9 g tannic acid equivalent (TAE), and IC50 of 44 ± 4 μg/mL.Conclusion: The findings of this study suggest that DCM extract may possess anticancer effect against lung adenocarcinoma and brain glioblastoma, as well as serve as an anti-inflammatory agent.Keywords: Galega officinalis L, Biological activity, Bioassay, Anticancer, Lung adenocarcinoma, Brain glioblastoma, Goat’s rue
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".