Capsaicin reduces PLGA‐induced fibrosis by promoting M2 macrophages and suppressing overall inflammatory Response
Bibliographic record
Abstract
Capsaicin reduced poly(lactic-co-glycolic) acid (PLGA)-induced fibrosis by promoting IL-10 secretion and suppressing alpha-smooth muscle actin (α-SMA) expression. The lifetime and efficacy of tissue engineering scaffolds are determined by the foreign body response. In this study, we investigated the in vitro and in vivo effects of capsaicin to reduce biomaterial-induced fibrosis. RAW 264.7 cells cultured on PLGA films with capsaicin responded with significant (p < 0.05) upregulation in M2 markers arginase-1 and IL-10 and downregulation of M1 markers iNOS and IL-12, demonstrating the potential of capsaicin to reduce PLGA-induced inflammation. Subsequent animal studies were conducted where PLGA and capsaicin-embedded PLGA discs were implanted in C57BL/6 mice for 2 and 14 days. Explanted capsaicin-embedded PLGA implants had 40% less collagen than PLGA-only implants. Capsaicin caused a 35% increase in IL-10 which played a key role in suppressing fibrosis. Macrophage phenotype markers in peritoneal cells and adherent cells were unaffected by capsaicin; however, capsaicin suppressed the myofibroblast marker α-SMA in adherent cells by day 14. Overall, our results revealed that capsaicin reduced biomaterial-induced fibrosis and demonstrates that capsaicin has the potential to extend the lifetime of a tissue engineering scaffold when used in long-term drug release applications from hydrophobic biomaterials. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A:2424-2432, 2018.
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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) | 0.001 | 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) | 0.001 | 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".