3D in vitro M2 macrophage model to mimic modulation of tissue repair
Bibliographic record
Abstract
Abstract Distinct anti-inflammatory macrophage (M2) subtypes, namely M2a and M2c, are reported to modulate the tissue repair process tightly and chronologically by modulating fibroblast differentiation state and functions. To establish a well-defined three-dimensional (3D) cell culture model to mimic the tissue repair process, we utilized THP-1 human monocytic cells and a 3D collagen matrix as a biomimetic tissue model. THP-1 cells were differentiated into macrophages, and activated using IL-4/IL-13 (M IL-4/IL-13 ) and IL-10 (M IL-10 ). Both activated macrophages were characterized by both their cell surface marker expression and cytokine secretion profile. Our cell characterization suggested that M IL-4/IL-13 and M IL-10 demonstrate M2a- and M2c-like subtypes, respectively. To mimic the initial and resolution phases during the tissue repair, both activated macrophages were co-cultured with fibroblasts and myofibroblasts. We showed that M IL-4/IL-13 were able to promote matrix synthesis and remodeling by induction of myofibroblast differentiation via transforming growth factor beta-1 (TGF-β1). On the contrary, M IL-10 demonstrated the ability to resolve the tissue repair process by dedifferentiation of myofibroblast via IL-10 secretion. Overall, our study demonstrated the importance and the exact roles of M2a and M2c-like macrophage subtypes in coordinating tissue repair in a biomimetic model. The established model can be applied for high-throughput platforms for improving tissue healing and anti-fibrotic drugs testing, as well as other biomedical studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".