Acellular Gelatinous Material of Human Umbilical Cord Enhances Wound Healing: A Candidate Remedy for Deficient Wound Healing
Bibliographic record
Abstract
Impaired wound healing is a severe clinical challenge and research into finding effective wound healing strategies is underway as there is no ideal treatment. Gelatinous material from the umbilical cord called Wharton’s jelly is a valuable source of mesenchymal stem cells which have been shown to aid wound healing. While the cellular component of Wharton’s jelly has been the subject of extensive research during the last few years, little is known about the de-cellularized jelly material of the umbilical cord. This is important as they are native niche of stem cells. We have isolated Wharton’s jelly from umbilical cords and then fractionated acellular gelatinous Wharton’s jelly (AGWJ). Here, we show for the first time that AGWJ enhances wound healing in vitro as well as in vivo for wounds in a murine model. In vivo staining of the wounds revealed a smaller wound length in the AGWJ treated wounds in comparison to control treatment by enhancing cell migration and differentiation. AGWJ significantly enhanced fibroblast cell migration in vitro. Aside from cell migration, AGWJ changed the cell morphology of fibroblasts to a more elongated phenotype, characteristic of myofibroblasts, confirmed by upregulation of alpha smooth muscle actin using immunoblotting. AGWJ treatment of wounds led to accelerated differentiation of cells into myofibroblasts, shortening the proliferation phase of wound healing. This data provides support for a novel wound healing remedy using AGWJ. AGWJ being native biological, cost effective and abundantly available globally, makes it a highly promising treatment option for wound dressing and skin regeneration.
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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".