Signaling Pathways in Root Resorption: Linking Inflammation, Odontoclastogenesis, and Tissue Remodeling
Bibliographic record
Abstract
INTRODUCTION: Root resorption is a pathologic process characterized by the breakdown of dentin and cementum by odontoclasts, mirroring the mechanisms of osteoclast-driven bone resorption. Osteoclastogenesis is tightly regulated by the RANK/RANKL/OPG axis and other signaling pathways, including Wnt, ATP-P2RX7-IL-1, programmed cell death, and inflammasome activation. OBJECTIVE: This review provides a comprehensive analysis of the key signaling pathways and molecular mediators orchestrating root resorption in orthodontic, traumatic, and inflammatory conditions. METHODS: A literature-based analysis was conducted, focusing on molecular and cellular mechanisms involved in root resorption. Key pathways such as RANK/RANKL/OPG, Wnt signaling, ATP-P2RX7-IL-1, and inflammasome activation were examined. The role of proinflammatory and anti-inflammatory cytokines, matrix metalloproteinases, and periostin were also analyzed. RESULTS: Proinflammatory mediators such as IL-1, IL-6, IL-8, tumor necrosis factor-alpha, IL-17, IL-22, and IL-23 drive odontoclastic differentiation, whereas anti-inflammatory cytokines, including IL-4, IL-10, and transforming growth factor-beta, counteract resorptive activity. Additionally, matrix metalloproteinases and periostin modulate extracellular matrix remodeling, impacting resorption progression. The balance between resorptive and reparative processes is influenced by the inflammatory microenvironment, fibroblast-macrophage interactions, and mechanotransduction. While the molecular mechanisms underlying odontoclastogenesis parallel bone resorption, unique features of root structures, such as the cementoid layer, contribute to resistance against resorption. CONCLUSION: The intricate cross talk between pro-resorptive and antiresorptive factors, emphasizing their roles in odontoclast activation and extracellular matrix remodeling, dictate the extent of root degradation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".