Comprehensive analysis of different solvent extracts of Ferula communis L. fruit reveals phenolic compounds and their biological properties via in vitro and in silico assays
Bibliographic record
Abstract
Although giant fennel is recognized as a "superfood" rich in phytochemicals with antioxidant activity, research into the antibacterial properties of its fruits has been relatively limited, compared to studies involving the root and aerial parts of the plant. In this study, seven solvents-acetone, methanol, ethanol, ethyl acetate, chloroform, water, and hexane-were used to extract the chemical constituents of the fruit of giant fennel (Ferula communis), a species of flowering plant in the carrot family Apiaceae. Specific attributes of these extracts were investigated using in silico simulations and in vitro bioassays. High-performance liquid chromatography equipped with a diode-array detector (HPLC-DAD) identified 15 compounds in giant fennel extract, with p-coumaric acid, 3-hydroxybenzoic acid, sinapic acid, and syringic acid being dominant. Among the solvents tested, ethanol demonstrated superior antioxidant activity and phenolic and flavonoid contents. F. communis extracts showed advanced inhibition of gram-negative pathogens (Escherichia coli and Proteus mirabilis) and variable antifungal activity against tested strains. Molecular docking simulations assessed the antioxidative, antibacterial, and antifungal properties of F. communis, facilitating innovative therapeutic development through predicted compound-protein interactions. In conclusion, the results validate the ethnomedicinal use and potential of F. communis. This highlights its significance in natural product research and ethnopharmacology.
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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 | 0.001 |
| 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".