Macrophage immunomodulation in chronic osteolytic diseases—the case of periodontitis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it