p75NTR silencing inhibits proliferation, migration, and extracellular matrix deposition of hypertrophic scar fibroblasts by activating autophagy through inhibiting the PI3K/Akt/mTOR pathway
Bibliographic record
Abstract
Hypertrophic scar (HS) results from abnormal wound healing, accompanied by excessive hypercellularity, migration, and extracellular matrix (ECM) deposition. Autophagy dysregulation plays crucial roles during HS formation. The overexpressed p75 neurotrophin receptor (p75NTR) in injured skin tissue after wound healing becomes a factor aggravating scar. This study was designed to investigate the role of p75NTR and p75NTR-mediated autophagy in the process of HS. The results revealed that p75NTR expression was significantly upregulated while that of autophagy proteins was downregulated in cicatrix at 3 and 6 months after a burn, which was recovered at 12 months. p75NTR silencing inhibited proliferation, migration, and ECM deposition of hypertrophic scar fibroblasts (HSF), whereas p75NTR overexpression presented the opposite results. Silencing of p75NTR reduced the expression of PI3K/Akt/mTOR signaling molecules while enhancing that of autophagy proteins. Importantly, PI3K agonist (IGF-1) intervention notably decreased the levels of LC3B II/I and Beclin-1 and restored the inhibitory effects of p75NTR silencing on proliferation, migration, and ECM deposition of HSF. Concurrently, autophagy inhibitor 3-methyladenine (3-MA) treatment exhibited the same variation trends with IGF-1. Taken together, these findings demonstrated that p75NTR silencing inhibits proliferation, migration, and ECM deposition of HSF by activating autophagy by inhibiting the PI3K/Akt/mTOR pathway.
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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".