NMR and GC-MS based metabolic profiling, total phenolic content, antibacterial, antioxidant, anticancer and In-silico antiviral activity of Origanum ramonense plant
Bibliographic record
Abstract
Origanum ramonense is a rare and underexplored aromatic (Lamiaceae family) native to the Mediterranean region, but despite its prospects as a medicinal plant, there is a shortage of spectrometry based metabolic profiling of this plant. Thus, the objective of this study was to carry out a detailed investigation of the metabolic profile of Origanum ramonense extracts using multiple solvents (methanol, methanol/water, ethyl acetate, dichloromethane/methanol, and hot water) of varying polarity, and further assess its bioactive potential. Using analytical tools such as NMR and GC/MS, we identified functional groups of plant metabolites and further employed multiple assays such as DPPH (antioxidant activity) and the Folin-Ciocalteu method to understand the plant's bioactivity. The extracts were found to be rich with polyphenolic compounds (17.8-107.2 mg Gallic acid per gram extract) and had strong antioxidant activity (IC50 5.8-128.5). The promising bioactivity was validated not only by results for in-vitro anticancer and (MCF-7 and HeLa cells) antibacterial tests (S. aureus and S. pneumoniae) but also in-silco molecular docking further showed the potential of antiviral activity of the extracted metabolites against SARS-CoV. These findings highlight O. ramonense as a valuable source of natural antioxidants and bioactive compounds, underscoring the need for further research into its medicinal properties.
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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 | 0.001 | 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".