Anti-microRNA-378a Enhances Wound Healing Process by Upregulating Integrin Beta-3 and Vimentin
Bibliographic record
Abstract
Delayed or impaired wound healing is a major public health issue worldwide, especially in patients with diabetes mellitus and vascular atherosclerosis. MicroRNAs have been identified as key regulators of wound healing. Here, we show that miR-Pirate378a transgenic mice (and thus have inhibited miR-378a-5p function) display enhanced wound healing. Expression of vimentin and β3 integrin, two important modulators of wound healing, is markedly elevated in the transgenic mice. MiR-Pirate378a-transfected cells display greater mobility during migration assays, which was hypothesized to be due to the upregulation of vimentin and β3 integrin. Both molecules were confirmed to be targets of miR-378a, and thus their expression could be rescued by miR-Pirate378a. Overexpression of vimentin also contributed to fibroblast differentiation, and upregulation of β3 integrin was responsible for increased angiogenesis. Mice treatment with miR-Pirate378a-conjugated nanoparticles displayed enhanced wound healing. Thus, we have demonstrated that knockdown of miR-378a increased the expression of its target proteins, vimentin, and β3 integrin, which accelerated fibroblast migration and differentiation in vitro and enhanced wound healing in vivo.
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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) | 0.002 | 0.001 |
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".