Exosomes from acellular Wharton’s jelly of the human umbilical cord promotes skin wound healing
Bibliographic record
Abstract
BACKGROUND: Compromised wound healing has become a global public health challenge which presents a significant psychological, financial, and emotional burden on patients and physicians. We recently reported that acellular gelatinous Wharton's jelly of the human umbilical cord enhances skin wound healing in vitro and in vivo in a murine model; however, the key player in the jelly which enhances wound healing is still unknown. METHODS: We performed mass spectrometry on acellular gelatinous Wharton's jelly to elucidate the chemical structures of the molecules. Using an ultracentrifugation protocol, we isolated exosomes and treated fibroblasts with these exosomes to assess their proliferation and migration. Mice were subjected to a full-thickness skin biopsy experiment and treated with either control vehicle or vehicle containing exosomes. Isolated exosomes were subjected to further mass spectrometry analysis to determine their cargo. RESULTS: Subjecting the acellular gelatinous Wharton's jelly to proteomics approaches, we detected a large amount of proteins that are characteristic of exosomes. Here, we show that the exosomes isolated from the acellular gelatinous Wharton's jelly enhance cell viability and cell migration in vitro and enhance skin wound healing in the punch biopsy wound model in mice. Mass spectrometry analysis revealed that exosomes of Wharton's jelly umbilical cord contain a large amount of alpha-2-macroglobulin, a protein which mimics the effect of acellular gelatinous Wharton's jelly exosomes on wound healing. CONCLUSIONS: Exosomes are being enriched in the native niche of the umbilical cord and can enhance wound healing in vivo through their cargo. Exosomes from the acellular gelatinous Wharton's jelly and the cargo protein alpha-2-macroglobulin have tremendous potential as a noncellular, off-the-shelf therapeutic modality for wound healing.
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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".