Alternatively activated macrophages derived from THP‐1 cells promote the fibrogenic activities of human dermal fibroblasts
Bibliographic record
Abstract
Macrophages play a key role in the wound healing process and can be divided into classically activated macrophages (M1) and alternatively activated macrophages (M2). Fibroblasts maintain the physical integrity of connective tissue, participate in wound closure as well as produce and remodel extracellular matrix. Macrophages have a close relationship with fibroblasts by increasing the production of matrix metalloproteinase-1 (MMP-1) for faster wound closure and remodeling and myofibroblast differentiation from fibroblasts. In this study, resting state (M0), M1 and M2 macrophages differentiated from the human monocytic THP-1 cell line were used to co-culture with human dermal fibroblasts (HDF) for 48, 96 and 144 hours to investigate the effect of macrophages subsets on the fibrogenic activity of fibroblasts. The differentiation and polarization from THP-1 cells to M0, M1 and M2 macrophages were characterized by flow cytometry and cell cycle analysis. Cell sorting was performed to purify M0 and M2 macrophages. Cell proliferation, collagen synthesis, myofibroblast formation, gene expression of anti-fibrotic and pro-fibrotic factors, MMP-1 activity, and cytokine concentration were investigated. Results showed differentiation of M0 and polarization of M1 and M2 macrophages. M2 macrophages promoted the fibrogenic activities of co-cultured HDF by facilitating cell proliferation, increasing the collagen content, alpha-smooth muscle actin expressed cells, expression of the pro-fibrotic genes and concentration of M2 macrophage related factors, as well as decreasing the expression of the anti-fibrotic genes and MMP-1 activity. These findings reinforce the pro-fibrotic role of M2 macrophages, suggesting therapeutic strategies in fibrotic diseases should target M2 macrophages in the future.
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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) | 0.003 | 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".