Oat-Derived Bioactives (Avenanthramide and Beta-glucan) Increase the Velocity of and Accelerate Wound Healing
Bibliographic record
Abstract
Objectives: Oats (Avena sativa) are an important whole grain with lipid lowering and anti-inflammatory effects. Recent studies revealed that oat-derived bioactives, avenanthramide (AVE) and beta-glucan (βG), stimulate wound healing by inducing angiogenesis and recruiting endothelial progenitor cells. We here report the effect of AVE and βG on healing velocity and acceleration, characterizing wound healing dynamics. Methods: AVE and βG extracted from whole oats were solubilized in buffer (0.1%, 0.5%, 1%) and applied by SQ injection to the margins of full-thickness excisional wounds on the dorsum of C57BL/6 mice every two days and compared to controls. Surface planimetry was assessed using digital photographs taken every two post-operative days. The velocity of wound healing (Δwound area/Δtime) induced by each bioactive was calculated by linear regression as the slope between time points; acceleration (Δvelocity/Δtime) was determined as a higher order curve. Results: Compared to controls, both AVE and βG resulted in greater healing velocity, with distinct temporal dynamics. An early effect was observed with βG (before POD 2) while AVE had a later effect on healing velocity (after POD 2). 1% AVE resulted in the highest maximum velocity compared to control (21.7 vs.11.7), a 1.9-fold faster healing speed. Both 1% and 0.1% βG achieved comparable maximum velocities (21.4 vs and 21.3) but earlier in the healing process. Analysis of wound healing acceleration revealed differential dynamics between bioactives. Conclusions: Oat bioactives augment normal wound healing by improving wound healing dynamics. This is the first description of wound healing velocity and acceleration induced by AVE and βG, correlated with faster wound closure in vivo. Velocity and acceleration are novel metrics that characterize the dynamic biological effect of oat bioactives beyond completed wound closure. Funding Sources: AVE and βG supplied by Ceapro, Inc., Edmonton, Alberta.
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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 | 0.000 |
| 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) | 0.000 | 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".