Cellular Crosstalk Between Periodontal Ligament Fibroblasts and Macrophages: Insights from 2D and 3D Culture Systems in External Inflammatory Root Resorption
Bibliographic record
Abstract
INTRODUCTION: Root resorption following traumatic dental injuries is a complex process involving the breakdown of root dentin and cementum by odontoclasts, which differentiate from precursor macrophages. This differentiation is regulated by periodontal ligament fibroblasts (PDLF). Macrophages can polarize into two types: proinflammatory (M1) and anti-inflammatory (M2), which can influence either disease progression or healing. METHODS: This review considers experimental models that have been utilized to study the interactions between PDLF and macrophages under normal and inflammatory conditions, including bacterial exposure and hypoxia. RESULTS: Under normal circumstances, PDLFmaintain the balance of periodontal tissues and the surrounding immune environment. However, during inflammatory conditions such as exposure to bacteria or hypoxia, injured PDLF interact with macrophages through signaling mechanisms that promote the differentiation of macrophages into odontoclasts. Various experimental models have been utilized to study the interactions between PDLF and macrophages. These interactions alter the balance of macrophage polarization, with M1 macrophages contributing to disease progression. CONCLUSION: This review highlights recent insights into the dynamic relationship between PDLF and macrophages in the context of external inflammatory root resorption, emphasizing the importance of their crosstalk in determining disease outcome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".