Effect of Human Wharton's Jelly Mesenchymal Stem Cell Paracrine Signaling on Keloid Fibroblasts
Bibliographic record
Abstract
Keloid scars are abnormal benign fibroproliferative tumors with high recurrence rates and no current efficacious treatment. Accumulating evidence suggests that human umbilical cord Wharton's jelly-derived mesenchymal stem cells (WJ-MSCs) have antifibrotic properties. Paracrine signaling is considered one of the main underlying mechanisms behind the therapeutic effects of mesenchymal stem cells. However, the paracrine signaling effects of WJ-MSCs on keloids have not yet been reported. The aim of this study is to investigate paracrine signaling effects of human WJ-MSCs on keloid fibroblasts in vitro. Human umbilical cords and keloid skin samples were obtained, and WJ-MSCs and keloid fibroblasts were isolated and cultured. One-way and two-way paracrine culture systems between both cell types were investigated. Plasminogen activator inhibitor-I and transforming growth factor-β2 (TGF-β2) transcripts were upregulated in keloid fibroblasts cultured with WJ-MSC-conditioned medium (WJ-MSC-CM) and cocultured with inserts, while showing lower TGF-β3 gene expression. Interleukin (IL)-6, IL-8, TGF-β1, and TGF-β2 protein expression was also enhanced. The WJ-MSC-CM-treated keloid fibroblasts showed higher proliferation rates than their control keloid fibroblasts with no significant change in apoptosis rate or migration ability. In our culture conditions, the indirect application of WJ-MSCs on keloid fibroblasts may enhance their profibrotic phenotype.
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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) | 0.002 | 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".