Fibroblast growth factor 23 overexpression impacts negatively on dentin mineralization and dentinogenesis in mice
Bibliographic record
Abstract
1. Though previous studies have shown that fibroblast growth factor 23 (FGF23) mRNA expression localizes in ameloblasts and odontoblasts in teeth, it is unclear what effect FGF23 overexpression has on dentin mineralization and dentinogenesis. Toward this end, the phenotypes of mandibles and teeth were compared between 6-week-old FGF23 transgenic mice and their wild-type littermates by radiography, microcomputed tomography scanning, histology, histochemistry and immunohistochemistry. 2. The mineral density was reduced in all teeth, including molars and incisors, and in the mandible, and the mineralized tooth volume in incisor and molars, and the mineralized cortical and alveolar bone volume in mandibles were decreased in FGF23 transgenic mice compared with their wild-type littermates. The dental volume, reparative dentin area, the expression of dentin sialoprotein in dentin, and the deposition of type I collagen and osteocalcin in the dental matrix were significantly reduced. However, the predentin volume and the expression of biglycan in dentin were increased in FGF23 transgenic mice compared with their wild-type littermates. 3. The results of the present study show that FGF23 overexpression plays a negative regulatory role on dentin mineralization and dentinogenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".