Relationships between protein and mineral during enamel development in normal and genetically altered mice
Bibliographic record
Abstract
The purpose of this study was to quantify and compare the amounts of volatiles (mostly protein) and mineral present in developing incisor enamel in normal mice and in those genetically engineered for absence of intact enamelin, ameloblastin, matrix metalloproteinase 20 (MMP20) or kallikrein-related peptidase 4 (KLK4). Data indicated that all mice showed peaks in the gross weight of volatiles and a similar weight of mineral at locations on incisors normally associated with early maturation. Thereafter, the content of volatiles on normal incisors declined rapidly by as much as 62%, but not by 100%, over 2 mm, accompanied by increases of ≈ threefold in mineral weights. Enamelin heterozygous mice (lower incisors) showed a decrease in volatile content across the maturation stage, yet mineral failed to increase significantly. Mmp20 null mice showed no significant loss of volatiles from maturing enamel, yet the amount of mineral increased. Klk4 null mice showed normal mineral acquisition up to early maturation, but the input of new volatiles in mid to late maturation caused the final mineralization to slow below normal levels. These results suggest that it is not only the amount of protein but also the nature or type of protein or fragments present in the local crystallite environment that affects their volumetric expansion as they mature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".