Natural essential oil mix of sweet orange peel, cumin, and allspice elicits anti-inflammatory activity and pharmacological safety similar to non-steroidal anti-inflammatory drugs
Bibliographic record
Abstract
An inflammation response occurs when the body reacts to exogenous and endo enous noxious stimuli, and it helps the body respond to infection and repair tissues, adapt to stress, and remove dead or damaged cells. Anti-inflammatory drugs such as non-steroidal anti-inflammatory drugs are traditionally used to treat inflammation; however, these drugs often cause negative side effects. For this reason, developing and establishing effective alternative medicines for treating many chronic diseases with underlying inflammation is critically dependent on the identification of new organic molecules and bioactive substances. Aromatic and volatile compounds found in essential oils isolated from Pimenta dioica (allspice), Cuminum cyminum (cumin), and Citrus sinensis (sweet orange) are a source of bioactive compounds. Allspice essential oil reduces ear inflammation more than 65% and the anti-inflammatory activity of allspice essential oil is enhanced when combined with sweet orange peel and cumin essential oils, resulting in the reduction of edema inflammation by more than 85%, similar to indomethacin. As an alternative to anti-inflammatory treatment, essential oil mix is pharmacologically safe as it is neither toxic nor mutagenic.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".