Adipose-derived mesenchymal stromal cells modulate tendon fibroblast responses to macrophage-induced inflammation in vitro
Bibliographic record
Abstract
INTRODUCTION: Macrophage-driven inflammation is a key feature of the early period following tendon repair, but excessive inflammation has been associated with poor clinical outcomes. Modulation of the inflammatory environment using molecular or cellular treatments may provide a means to enhance tendon healing. METHODS: To examine the effect of pro-inflammatory cytokines secreted by macrophages on tendon fibroblasts (TF), we established in vitro models of cytokine and macrophage-induced inflammation. Gene expression, protein expression, and cell viability assays were used to examine TF responses. In an effort to reduce the negative effects of inflammatory cytokines on TFs, adipose-derived mesenchymal stromal cells (ASCs) were incorporated into the model and their ability to modulate inflammation was investigated. RESULTS: The inflammatory cytokine interleukin 1 beta (IL-1β) and macrophages of varying phenotypes induced up-regulation of pro-inflammatory factors and matrix degradation factors and down-regulation of factors related to extracellular matrix formation by TFs in culture. ASCs did not suppress these presumably negative effects induced by IL-1β. However, ASC co-culture with M1 (pro-inflammatory) macrophages successfully suppressed the effects of M1 macrophages on TFs by inducing a phenotypic switch from a pro-inflammatory macrophage phenotype to an anti-inflammatory macrophage phenotype, thus resulting in exposure of TFs to lower levels of pro-inflammatory cytokines (e.g., IL-1β, tumor necrosis factor alpha (TNFα)). CONCLUSIONS: These findings suggest that IL-1β and M1 macrophages are detrimental to tendon healing and that ASC-mediated modulation of the post-operative inflammatory response may be beneficial for tendon healing.
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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 | 0.001 |
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".