Resolving Macrophages Counter Osteolysis by Anabolic Actions on Bone Cells
Bibliographic record
Abstract
Progression of inflammatory osteolytic diseases, including rheumatoid arthritis and periodontitis, is characterized by increased production of proinflammatory mediators and matrix-degrading enzymes by macrophages and increased osteoclastic activity. Phenotypic changes in macrophages are central to the healing process in virtually all tissues. Using a murine model of periodontitis, we assessed the timing of macrophage phenotypic changes and the impact of proresolving activation during inflammatory osteolysis and healing. Proinflammatory macrophage activation and TNF-α overproduction within 3 wk after induction of periodontitis was associated with progressing bone loss. Proresolving activation within 1 wk of stimulus removal and markers of resolving macrophages (IL-10, TGF-β, and CD206) correlated strongly with bone levels. In vivo macrophage depletion with clodronate liposomes prevented bone resorption but impaired regeneration. Induction of resolving macrophages with rosiglitazone, a PPAR-γ agonist, led to reduced bone resorption during inflammatory stimulation and increased bone formation during healing. In vitro assessment of primary bone marrow-derived macrophages activated with either IFN-γ and LPS (proinflammatory activation) or IL-4 (proresolving activation) showed that IL-4-activated cells have enhanced resolving functions (production of anti-inflammatory cytokines; migration and phagocytosis of aged neutrophils) and exert direct anabolic actions on bone cells. Cystatin C secreted by resolving but not inflammatory macrophages explained, in part, the macrophage actions on osteoblasts and osteoclasts. This study supports the concept that therapeutic induction of proresolving functions in macrophages can recouple bone resorption and formation in inflammatory osteolytic diseases.
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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 | 0.001 |
| 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".