Quantitative Study of the Interaction of Salivary Acidic Proline-Rich Proteins with Hydroxyapatite
Bibliographic record
Abstract
Using specific antisera to the proline-rich proteins A and C, a quantitative assay has been developed for human acidic proline-rich proteins. By adding varying amounts of hydroxyapatite to a given volume of saliva, the adsorption of acidic proline-rich proteins and total protein to hydroxyapatite has been determined for stimulated and unstimulated parotid and submandibular saliva. The maximum amount of acidic proline-rich proteins adsorbed (ADSmax) varied from 85 to 97% of the total acidic proline-rich proteins present in unadsorbed saliva. ADSmax for total protein varied from 41 to 79%. The amount of hydroxyapatite needed for half maximal adsorption (HA50) was consistently smaller for acidic proline-rich proteins than for total protein and for the secretions from any gland, HA50 was consistently smaller for unstimulated than stimulated saliva. HA50 values from one individual were consistently smaller than the corresponding HA50 values from a second individual. The mean concentration of acidic proline-rich proteins in unadsorbed saliva was 50 ± 26 mg/l00 ml and the mean contribution of acidic proline-rich proteins to hydroxyapatite-adsorbed proteins was 42% ± SD 23 of total adsorbed proteins. On the basis of equilibrium dialysis experiments with purified protein A and C in buffers with pH and ionic strength similar to salivary secretions, it was estimated that the concentration of calcium bound to acidic proline-rich proteins varied from 8 μM in unstimulated submandibular saliva to 41 μM in stimulated submandibular saliva. The concentration of calcium bound to acidic proline-rich proteins therefore apparently depends on the type of salivary secretion.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".