Study on antioxidant and antimicrobial potential of chemically profiled essential oils extracted from <i>Juniperus phoenicea</i> (L.) by use of <i>in vitro</i> and <i>in silico</i> approaches
Bibliographic record
Abstract
Abstract Juniperus phoenicea (L.) is a medicinal plant that has been used in phytotherapy as a treatment of certain pathological infections. In this context, the present work aimed to valorize the essential oil of J. phoenicea seeds (EOGP) by studying its chemical composition, and antioxidant and antimicrobial activities. The EOGP was extracted by use of hydrodistillation and characterized by gas chromatography (GC–MS). The antioxidant power was evaluated by three methods (TAC, DPPH, and FRAP). The antimicrobial power was evaluated against Staphylococcus aureus (ATCC6633), Escherichia coli (K12), Bacillus subtilis (DSM6333), Proteus mirabilis (ATCC29906), Candida albicans (ATCC10231), Aspergillus niger (MTCC282), Aspergillus flavus (MTCC9606), and Fusarium oxysporum (MTCC9913). The GC/MS results revealed a total identification of 99.98% with a dominance of carvacrol (39.81%) followed by p -cymen-3-ol (34.44%) and o -cymene (13.60%). Findings showed that EOGP exhibited important antioxidant power as IC50 was determined to be 26 µg/mL for 2,2-diphenyl-1-picrylhydrazyl, while EC50 was 216.34 µg/mL for ferric reducing antioxidant power and total antioxidant capacity was 720 mg AAE/g. The antimicrobial power on solid medium revealed that the inhibition diameters ranged from 11.30 ± 0.58 to 20 mm for the bacterial strains and from 9.33 ± 0.57 to 54.43 ± 0.29 mm for fungi. Notably, minimum inhibitory concentrations ranged from 18 to 19 µg/mL for bacterial strains and from 5.04 to 10.09 µg/mL for fungal strains. Overall, our results demonstrated the importance of EOGP as a source of natural antioxidant and antibacterial medicines against clinically relevant pathogenic strains.
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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 | 0.000 |
| 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".