Bibliographic record
Abstract
Apical periodontitis is the inflammation and destruction of periradicular tissues, mediated by microbial factors originating from the infected pulp space. This bacteria-mediated inflammatory disease is known to interfere with root development in immature permanent teeth. Current research on interventions in immature teeth has been dedicated to facilitating the continuation of root development as well as regenerating the dentin-pulp complex, but the fundamental knowledge on the cellular interactions and the role of periapical mediators in apical periodontitis in immature roots that govern the disease process and post-treatment healing is limited. The limitations in 2D monolayer cell culture have a substantial role in the existing limitations of understanding cell-to-cell interactions in the pulpal and periapical tissues. Three-dimensional (3D) tissue constructs with two or more different cell populations are a better physiological representation of in vivo environment. These systems allow the high-throughput testing of multi-cell interactions and can be applied to study the interactions between stem cells and immune cells, including the role of mediators/cytokines in simulated environments. Well-designed 3D models are critical for understanding cellular functions and interactions in disease and healing processes for future therapeutic optimization in regenerative endodontics. This narrative review covers the fundamentals of (1) the disease process of apical periodontitis; (2) the influence and challenges of regeneration in immature roots; (3) the introduction of and crosstalk between mesenchymal stem cells and macrophages; (4) 3D cell culture techniques and their applications for studying cellular interactions in the pulpal and periapical tissues; (5) current investigations on cellular interactions in regenerative endodontics; and, lastly, (6) the dental-pulp organoid developed for regenerative endodontics.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".