Carvacrol and human health: A comprehensive review
Bibliographic record
Abstract
Carvacrol (CV) is a phenolic monoterpenoid found in essential oils of oregano (Origanum vulgare), thyme (Thymus vulgaris), pepperwort (Lepidium flavum), wild bergamot (Citrus aurantium bergamia), and other plants. Carvacrol possesses a wide range of bioactivities putatively useful for clinical applications such antimicrobial, antioxidant, and anticancer activities. Carvacrol antimicrobial activity is higher than that of other volatile compounds present in essential oils due to the presence of the free hydroxyl group, hydrophobicity, and the phenol moiety. The present review illustrates the state-of-the-art studies on the antimicrobial, antioxidant, and anticancer properties of CV. It is particularly effective against food-borne pathogens, including Escherichia coli, Salmonella, and Bacillus cereus. Carvacrol has high antioxidant activity and has been successfully used, mainly associated with thymol, as dietary phytoadditive to improve animal antioxidant status. The anticancer properties of CV have been reported in preclinical models of breast, liver, and lung carcinomas, acting on proapoptotic processes. Besides the interesting properties of CV and the toxicological profile becoming definite, to date, human trials on CV are still lacking, and this largely impedes any conclusions of clinical relevance.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".