Suppression of Lipopolysaccharide‐stimulated Cytokine/Chemokine Production in Skin Cells by Sandalwood Oils and Purified α‐santalol and β‐santalol
Bibliographic record
Abstract
Medicinally, sandalwood oil (SO) has been attributed with antiinflammatory properties; however, mechanism(s) for this activity have not been elucidated. To examine how SOs affect inflammation, cytokine antibody arrays and enzyme-linked immunosorbent assays were used to assess changes in production of cytokines and chemokines by co-cultured human dermal fibroblasts and neo-epidermal keratinocytes exposed to lipopolysaccharides and SOs from Western Australian and East Indian sandalwood trees or to the primary SO components, α-santalol and β-santalol. Lipopolysaccharides stimulated the release of 26 cytokines and chemokines, 20 of which were substantially suppressed by simultaneous exposure to either of the two sandalwood essential oils and to ibuprofen. The increased activity of East Indian SO correlated with increased santalol concentrations. Purified α-santalol and β-santalol equivalently suppressed production of five indicator cytokines/chemokines at concentrations proportional to the santalol concentrations of the oils. Purified α-santalol and β-santalol also suppressed lipopolysaccharide-induced production of the arachidonic acid metabolites, prostaglandin E2, and thromboxane B2, by the skin cell co-cultures. The ability of SOs to mimic ibuprofen non-steroidal antiinflammatory drugs that act by inhibiting cyclooxygenases suggests a possible mechanism for the observed antiinflammatory properties of topically applied SOs and provides a rationale for use in products requiring antiinflammatory effects.
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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".