The location and characteristics of two populations of dental pulp cells affect tooth development
Bibliographic record
Abstract
This study investigated the characteristics of two dental pulp cell populations during the early stages of crown formation in porcine teeth. A transplantation method was developed to reproduce epithelial cell-mesenchymal cell interactions during odontogenesis (tooth development). The technique allowed two types of cells/tissue to be combined in vivo. Populations of cells localized in the cervical loop epithelium region, dental pulp horn, and dental pulp core chambers were isolated and dissociated into single cells. Each population was examined for its gene-expression pattern using both semiquantitative and quantitative reverse transcription-polymerase chain reaction (RT-PCR) analyses, and for its tissue-formation capability by combining the cervical loop epithelial cells with either pulp horn cells or pulp core cells on biodegradable collagen scaffolds that were subsequently examined using histology and immunohistology. Gene-expression patterns showed that pulp horn cells were more mature than pulp core cells. Cervical loop epithelial cells combined with pulp horn cells mainly reconstituted dentin-cementum structures. By contrast, cervical loop epithelial cells combined with pulp core cells reconstituted enamel-dentin structures. These results suggest that mesenchymal cells residing in a specific location of the pulp possess a specific tissue-formation potential when combined with epithelial cells.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".