Hydroxyapatite as Remineralization Agent for Children's Dental Care
Bibliographic record
Abstract
Children are prone to develop dental caries. This is supported by epidemiological data confirming early childhood caries (ECC) as a highly prevalent disease affecting more than every second child worldwide. ECC is known to result from an imbalance between re- and demineralization where demineralization dominates due to frequent acid production by cariogenic bacteria present in oral biofilms. The application of oral care formulations containing remineralizing agents helps to prevent dental caries. As young children are sensitive and usually swallow (intended or unintended) a majority of toothpaste or other oral care products during daily dental care, all ingredients, especially the actives, should be non-toxic. Biomimetic hydroxyapatite [HAP; Ca 5 (PO 4 ) 3 (OH)] is known to have favorable remineralizing properties combined with an excellent biocompatibility, i.e., it is safe if accidently swallowed. Several clinical trials as well as in situ and in vitro studies have shown that HAP remineralizes enamel and dentin. Remineralization occurs due to deposition of HAP particles on tooth surfaces forming mineral-mineral bridges with enamel crystals, but also indirectly through calcium and phosphate ions release as well as HAP's buffering properties in acidic environments (i.e., in plaque). HAP induces a homogenous remineralization throughout the subsurface enamel lesions. This review summarizes the current evidence showing HAP as an effective remineralizing agent in oral care products for children. Additional studies showing also further beneficial effects of HAP such as the reduction of biofilm formation and the relief of hypersensitivity in children with molar incisor hypomineralization (MIH). It can be concluded that HAP is an effective and safe remineralizing agent for child dental care.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".