Specific Sizes of Hyaluronan Oligosaccharides Stimulate Fibroblast Migration and Excisional Wound Repair
Bibliographic record
Abstract
The extracellular matrix polysaccharide hyaluronan (HA) plays a key role in both fibrotic and regenerative tissue repair. Accumulation of high molecular weight HA is typical of regenerative repair, which is associated with minimal inflammation and fibrosis, while fragmentation of HA is typical of postnatal wounds, which heal in the presence of inflammation and transient fibrosis. It is generally considered that HA oligosaccharides and fragments of a wide size range support these processes of adult, fibrotic wound repair yet the consequences of sized HA fragments/oligosaccharides to each repair stage is not well characterized. Here, we compared the effects of native HA, HA oligosaccharide mixtures and individual sizes (4-10 mer oligosaccharides, 5 and, 40 kDa) of HA oligosaccharides and fragments, on fibroblast migration in scratch wound assays and on excisional skin wound repair in vivo. We confirm that 4-10 mer mixtures significantly stimulated scratch wound repair and further report that only the 6 and 8 mer oligosaccharides in this mixture are responsible for this effect. The HA 6 mer promoted wound closure, accumulation of wound M1 and M2 macrophages and the M2 cytokine TGFβ1, but did not increase myofibroblast differentiation. The effect of 6 mer HA on wound closure required both RHAMM and CD44 expression. In contrast, The 40 kDa HA fragment inhibited wound closure, increased the number of wound macrophages but had no effect on TGFβ1 accumulation or subsequent fibrosis. These results show that specific sizes of HA polymer have unique effects on postnatal wound repair. The ability of 6 mer HA to promote wound closure and inflammation resolution without increased myofibroblast differentiation suggests that this HA oligosaccharide could be useful for treatment of delayed or inefficient wound repair where minimal fibrosis is advantageous.
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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".