Macrophage immunomodulation in chronic osteolytic diseases—the case of periodontitis
Bibliographic record
Abstract
Periodontitis (PD) is a chronic osteolytic disease that shares pathogenic inflammatory features with other conditions associated with nonresolving inflammation. A hallmark of PD is inflammation-mediated alveolar bone loss. Myeloid cells, in particular polymorphonuclear neutrophils (PMN) and macrophages (Mac), are essential players in PD by control of gingival biofilm pathogenicity, activation of adaptive immunity, as well as nonresolving inflammation and collateral tissue damage. Despite mounting evidence of significant innate immune implications to PD progression and healing after therapy, myeloid cell markers and targets for immune modulation have not been validated for clinical use. The remarkable plasticity of monocytes/Mac in response to local activation factors enables these cells to play central roles in inflammation and restoration of tissue homeostasis and provides opportunities for biomarker and therapeutic target discovery for management of chronic inflammatory conditions, including osteolytic diseases such as PD and arthritis. Along a wide spectrum of activation states ranging from proinflammatory to pro-resolving, Macs respond to environmental changes in a site-specific manner in virtually all tissues. This review summarizes the existing evidence on Mac immunomodulation therapies for osteolytic diseases in the broader context of conditions associated with nonresolving inflammation, and discusses osteoimmune implications of Macs in PD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".