GC–MS Based Metabolites Profiling, In Vitro Antioxidant, Antibacterial, and Anti-Cancer Properties of Different Solvent Extracts from Leaves, Stems, Roots, and Flowers of <em>Micromeria fruticosa</em> (Lamiaceae)
Bibliographic record
Abstract
This study assesses the secondary metabolites, minerals, antimicrobial, antioxidant, and anticancer properties of Micromeria fruticosa plant different botanical parts (leaf, stem, root, flower) extracted with various solvents. The plant samples were sequentially obtained using different solvents (n-hexane, ethanol and water) through steeping. Then, each of the extracts was further analyzed by using gas chromatography–mass spectrometry (GC-MS). Moreover, the extracts were bio-assayed to test their antioxidant, antibacterial, and anti-cancer activities. Quali-quantitative analysis of M. fruticosa crude extracts revealed the occurrence of 27 secondary metabolites were identified including mainly monoterpenes, sesquterpenes, and fatty acids, with varying quantities. Some of the major bioactive compounds included, Menthone (5.42-30.05%), Oleamide (3.40-32.20%), Pulegone (10.66-64.1%), and Menthol (3.61-100.0%), which were detected mostly in all plant parts with significant quantities. Several antioxidant minerals, mainly, Fe, Zn, and Mn, were detected with the highest amounts in the Micromeria water extracts. Results from antimicrobial assays showed that the water extract of leaves exhibited the highest DPPH scavenging activity (89.73%) followed by the water extract of flowers (80.07%) at a concentration of 100 μg/mL. The water extract of stems showed greater antimicrobial activity against all the tested gram negative and positive bacteria (Methicillin-resistant Staphylococcus aureus, Staphylococcus aureus, Escherichia coli, and Shigella sonnie). The leaves ethanolic and stem aqueous extracts had a strong antimicrobial activity against E. coli. and C. albicans. Flower aqueous extract demonstrated the highest cytostatic effect on the colon cell line by reducing viability up to 30.4%, followed by the leaf ethanol extract with 38.6% cell viability reduction at 1000 µg/mL. In conclusion, extraction solvents influenced the recovery of phytocompounds and the highest pharmacological activities of the different extracts could be correlated to the presence of additional bioactive compounds. Our results suggest that M. fruticosa plant is a promising source of natural products with antioxidant, anti-microbial and anti-cancer properties for potential nutraceutical, therapeutic, and functional food applications. , , , , , ,
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 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 | 0.001 | 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".