Salivary Concentrations of Urea Released from a Chewing Gum Containing Urea and how These Affect the Urea Content of Gel–Stabilized Plaques and Their pH after Exposure to Sucrose
Bibliographic record
Abstract
The objectives were to: (1) determine the salivary concentrations of urea during 20 min chewing of a sugar-free gum containing 30 mg of urea; (2) measure the degree to which this urea would diffuse into a gel-stabilized plaque; (3) study the effect of the urea on the fall and subsequent rise in pH (Stephan curve) on exposure to 10% sucrose for 1 min; (4) model the measurements 2 and 3 mathematically. In point 1, the salivary urea concentration of the 12 subjects peaked at 47 mmol/l in the first 2 min of gum chewing, falling within 15 min to the unstimulated salivary concentration of 3.4 mmol/l. Recovery of urea from the saliva averaged 81.5%. 'Plaques' of 1% agarose or 67% dead bacteria in agarose accumulated urea from the saliva roughly as expected, whereas those plaques containing 8% live and 59% dead Streptococcus vestibularis showed negligible accumulation. Computer modelling showed this difference to be due to urease of live bacteria breaking down the urea as rapidly as it entered the plaque. Simulation of the effect of gum chewing subsequent to initiation of a Stephan curve in the latter type of plaque showed a rapid rise in pH but then a fall again on return to unstimulated conditions. This fall had not been seen in previous studies, with Streptococcus oralis, nor was it predicted by the computer modelling. Neither experimental simulation nor computer modelling suggested that chewing urea-containing gum before exposure to sucrose would have any effect on a subsequent Stephan curve. Thus chewing gum is only likely to inhibit caries when it is chewed after consumption of fermentable carbohydrate, rather than before.
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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 | 0.000 | 0.001 |
| 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 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".