Exosomes of human adipose stem cells mitigate irradiation injury to salivary glands by inhibiting epithelial-mesenchymal transition through miR-199a-3p targeting Twist1 and regulating TGFβ1/Smad3 pathway
Bibliographic record
Abstract
Rationale: Currently, irradiation-injured salivary glands (IR-SG) lack effective clinical treatment options.Emerging treatments using exosomes (Exo) have shown promising outcomes for various diseases.However, the efficacy of exosome in treating IR-SG remains unexplored.This study aimed to use exosomes to restore IR-SG function and to explore their underlying mechanisms.Methods: Exosomes isolated from human adipose-derived stem cell (ADSC-Exo) were injected into C57BL/6 mice that had their salivary glands injured with 14Gy.RNA sequencing profiled differentially expressed miRNAs and mRNAs of IR-SG.Epithelial-mesenchymal transition (EMT) mechanisms were further examined using SMG-C6 cells.Results: Exo-treated mice had a 96% increase in saliva secretion, higher cell proliferation, upregulated tissue repair/regeneration genes, and preserved functional cells with fewer collagen fibers compared to saline-treated mice.Exo treatment increased the expression of epithelial cell markers while decreasing mesenchymal cell markers.Notably, miR-199a-3p was significantly upregulated in Exo-treated mice, promoting cell growth and reducing EMT.Twist1, an EMT transcription factor, was identified as a direct target of miR-199a-3p and confirmed by luciferase assays.Twist1 overexpression promoted EMT, but Exo treatment or Twist1 knockdown reduced EMT marker expression and inactivated the TGFβ1/Smad3 pathway.Conclusions: ADSC-Exo is a promising therapy for IR-SG, primarily by mitigating EMT through miR-199a-3p targeting Twist1 and regulating the TGFβ1/Smad3 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 | 0.001 |
| 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".