Hyaluronan oligosaccharides are potential stimulators to angiogenesis via RHAMM mediated signal pathway in wound healing
Bibliographic record
Abstract
PURPOSE: To determine if oligosaccharides of hyaluronan (o-HA) promotes wound recovery by accelerating angiogenesis and to study the mechanisms by which o-HA stimulates endothelial cell (EC) proliferation. METHODS: Using hyaluronidase digestion, we prepared a mixture of hyaluronan (HA) fragments sizesd 2 to 10 disaccharides units, and studied their effects on EC growth and migration in mimicking wound recovery in vitro. The effects of o-HA on EC growth in vitro were studied by counting cell numbers. The roles of 2 hyaluronan receptors on EC cells, CD44 and RHAMM (Receptor for HA-Mediated Motility), were studied in initiating signaling cascades, using immunoblot assay. Signal transduction was determined by blocking antibodies to CD44 and RHAMM. An in vitro wound healing model was prepared by scratching the cellular layer of cultured EC, and movement of cells into the denuded area was quantified. RESULTS: o-HA was a strong stimulator to EC proliferation at low concentration 10microg/ml compared with native high molecular weight HA (n-HA) (P < 0.01). Signal transduction may be initiated by o-HA via RHAMM receptor on EC membrane, but not CD44. In the in vitro model, the lesion area was nearly completely recovered when the EC layer was exposed to o-HA 40hrs post-injury, whereas the wound area remained half recovered pretreated with native undegraded large HA and control medium.(P < 0.05 from 24 to 40hrs). CONCLUSION: Hyaluronan oligosaccharides may play a role in wound healing by increasing angiogenesis. o-HA-RHAMM binding dependent signal transduction pathway may be important in the regulation of angiogenesis associated with EC proliferation.
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 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".