Mouse Dspp frameshift model of human dentinogenesis imperfecta
Bibliographic record
Abstract
Abstract Non-syndromic inherited defects of tooth dentin are caused by two classes of dominant negative/gain-of-function mutations in dentin sialophosphoprotein ( DSPP ): 5′ mutations affecting an N-terminal targeting sequence and 3′ mutations that shift translation into the − 1 reading frame. DSPP defects cause an overlapping spectrum of phenotypes classified as dentin dysplasia type II and dentinogenesis imperfecta types II and III. Using CRISPR/Cas9, we generated a Dspp −1fs mouse model by introducing a FLAG-tag followed by a single nucleotide deletion that translated 493 extraneous amino acids before termination. Developing incisors and/or molars from this mouse and a Dspp P19L mouse were characterized by morphological assessment, bSEM, nanohardness testing, histological analysis, in situ hybridization and immunohistochemistry. Dspp P19L dentin contained dentinal tubules but grew slowly and was softer and less mineralized than the wild-type. Dspp P19L incisor enamel was softer than normal, while molar enamel showed reduced rod/interrod definition. Dspp −1fs dentin formation was analogous to reparative dentin: it lacked dentinal tubules, contained cellular debris, and was significantly softer and thinner than Dspp +/+ and Dspp P19L dentin. The Dspp −1fs incisor enamel appeared normal and was comparable to the wild-type in hardness. We conclude that 5′ and 3′ Dspp mutations cause dental malformations through different pathological mechanisms and can be regarded as distinct disorders.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".