IL‐6 counteracts the inhibitory effect of IL‐4 on osteogenic differentiation of human adipose stem cells
Bibliographic record
Abstract
Abstract Fracture repair is characterized by cytokine production and hypoxia. To better predict cytokine modulation of mesenchymal stem cell (MSC)‐aided bone healing, we investigated whether interleukin 4 (IL‐4), IL‐6, and their combination, affect osteogenic differentiation, vascular endothelial growth factor (VEGF) production, and/or mammalian target of rapamycin complex 1 (mTORC1) activation by MSCs under normoxia or hypoxia. Human adipose stem cells (hASCs) were cultured with IL‐4, IL‐6, or their combination for 3 days under normoxia (20% O 2 ) or hypoxia (1% O 2 ), followed by 11 days without cytokines under normoxia or hypoxia. Hypoxia did not alter IL‐4 or IL‐6‐modulated gene or protein expression by hASCs. IL‐4 alone decreased runt‐related transcription factor 2 (RUNX2) and collagen type 1 (COL1) gene expression, alkaline phosphatase (ALP) activity, and VEGF protein production by hASCs under normoxia and hypoxia, and decreased mineralization of hASCs under hypoxia. In contrast, IL‐6 increased mineralization of hASCs under normoxia, and enhanced RUNX2 gene expression under normoxia and hypoxia. Neither IL‐4 nor IL‐6 affected phosphorylation of the mTORC1 effector protein P70S6K. IL‐4 combined with IL‐6 diminished the inhibitory effect of IL‐4 on ALP activity, bone nodule formation, and VEGF production, and decreased RUNX2 and COL1 expression, similar to IL‐4 alone, under normoxia and hypoxia. In conclusion, IL‐4 alone, but not in combination with IL‐6, inhibits osteogenic differentiation and angiogenic stimulation potential of hASCs under normoxia and hypoxia, likely through pathways other than mTORC1. These results indicate that cytokines may differentially affect bone healing and regeneration when applied in isolation or in combination.
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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 | 0.000 |
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".