Long non-coding RNA H19 acts as a microRNA-194 sponge to inhibit the apoptosis and promote the proliferation of hypertrophic scar fibroblasts
Bibliographic record
Abstract
The effects of long non-coding RNAs (lncRNAs) on the proliferation of hypertrophic scars have been described, however, the underlying mechanisms are not well characterized. The present study aimed to investigate the mechanisms of lncRNA H19 in hypertrophic scars. The effects of the lncRNA H19 on the proliferation and apoptosis of hypertrophic scar fibroblasts (HSFs) were analyzed using 5′-ethynyl-2′-deoxyuridine staining, flow cytometry, and 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT). The results revealed H19 promoted the proliferation and inhibited the apoptosis in HSF. In addition, the binding associations between H19 and microRNA-194 (miR-194), and miR-194 and insulin-like growth factor I receptor (IGF1R) were identified using bioinformatics screening and verified using dual-luciferase assays. Furthermore, the effects of the IGF1R knockdown on H19-induced HSF phenotypes and regulation over the p38 MAPK pathway were determined. Mechanistically, miR-194 was identified as the downstream effector of the H19-mediated phenotypes of HSFs through its ability to directly target IGF1R, thus modulating the p38 MAPK signaling pathway. In conclusion, the findings suggested that H19 may inhibit the apoptosis and promote the proliferation of HSFs through the miR-194/IGF1R/p38 MAPK signaling axis, thereby contributing to the progression of hypertrophic scars. These findings may provide novel targets for the treatment of hypertrophic scars.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".