Ratite oils promote keratinocyte cell growth and inhibit leukocyte activation
Bibliographic record
Abstract
Traditionally, native Australian aborigines have used emu oil for the treatment of inflammation and to accelerate wound healing. Studies on mice suggest that topically applied emu oil may have anti-inflammatory properties and may promote wound healing. We investigated the effects of ratite oils (6 emu, 3 ostrich, 1 rhea) on immortalized human keratinocytes (HaCaT cells) in vitro by culturing the cells in media with oil concentrations of 0%, 0.5%, and 1.0%. Peking duck, tea tree, and olive oils were used as comparative controls. The same oils at 0.5% concentration were evaluated for their influence on peripheral blood mononuclear cell (PBMC) survival over 48 hr and their ability to inhibit IFNγ production in PBMCs activated by phytohemagglutinin (PHA) in ELISpot assays. Compared to no oil control, significantly shorter population doubling time durations were observed for HaCaT cells cultured in emu oil (1.51×faster), ostrich oil (1.46×faster), and rhea oil (1.64×faster). Tea tree oil demonstrated significant antiproliferative activity and olive oil significantly prolonged (1.35×slower) cell population doubling time. In contrast, almost all oils, particularly tea tree oil, significantly reduced PBMC viability. Different oils had different levels of inhibitory effect on IFNγ production with individual emu, ostrich, rhea, and duck oil samples conferring full inhibition. This preliminary investigation suggests that emu oil might promote wound healing by accelerating the growth rate of keratinocytes. Combined with anti-inflammatory properties, ratite oil may serve as a useful component in bandages and ointments for the treatment of wounds and inflammatory skin conditions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".