Novel trends, challenges and new perspectives for enamel repair and regeneration to treat dental defects
Bibliographic record
Abstract
Dental enamel is the hardest tissue in the human body, providing external protection for the tooth against masticatory forces, temperature changes and chemical stimuli. Once enamel is damaged/altered by genetic defects, dental caries, trauma, and/or dental wear, it cannot repair itself due to the loss of enamel producing cells following the tooth eruption. The current restorative dental materials are unable to replicate physico-mechanical, esthetic features and crystal structures of the native enamel. Thus, development of alternative approaches to repair and regenerate enamel defects is much needed but remains challenging due to the structural and functional complexities involved. This review paper summarizes the clinical aspects to be taken into consideration for the development of optimal therapeutic approaches to tackle dental enamel defects. It also provides a comprehensive overview of the emerging acellular and cellular approaches proposed for enamel remineralization and regeneration. Acellular approaches aim to artificially synthesize or re-mineralize enamel, whereas cell-based strategies aim to mimic the natural process of enamel development given that epithelial cells can be stimulated to produce enamel postnatally during the adult life. The key issues and current challenges are also discussed here, along with new perspectives for future research to advance the field of regenerative dentistry.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".