Bibliographic record
Abstract
Dental erosion is an increasing problem in many countries around the world, and research in this field has increased dramatically in recent years. Dental erosion is the dissolution of tooth tissues (enamel and dentine) by acids that are not of bacterial origin; most commonly these originate from the diet. Recently, protein-based technologies including casein and casein phosphopeptide (CPP) have been investigated and found to have erosion-inhibiting properties. The aim of this chapter is to summarize recent studies by the authors’ research groups to investigate the use of casein and casein-derived proteins as agents to inhibit dental erosion. A number of in vitro techniques and models are employed to investigate erosion, including non-contact optical profilometry, atomic-force microscopy (AFM) nanoindentation and hydroxyapatite dissolution rate experiments (hydroxyapatite is the main mineral component of teeth). Evidence is given for an almost instantaneous ‘protective’ effect afforded by casein, preventing demineralization of enamel under erosive conditions. Aqueous solutions of casein of 0.5 % w/v applied topically before an erosive challenge are shown to afford between 39 to 45 % reduction in demineralization over a range of clinically relevant timescales. The mechanistic aspects of the ‘protective effect’ afforded by casein are investigated using AFM and X-ray reflectometry (XRR). Data from both of these techniques show the formation of a thin (~ 6.6 nm) film of casein proteins on mica, a molecularly smooth mineral used as a model substrate.
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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 | 0.001 |
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".