The Role of SIBLING Proteins in Dental, Periodontal, and Craniofacial Development
Bibliographic record
Abstract
The majority of dental, periodontal, and craniofacial tissues are derived from the neural crest cells and ectoderm. Neural crest stem cells are pluripotent, capable of differentiating into a variety of cells. These cells can include osteoblasts, odontoblasts, cementoblasts, chondroblasts, and fibroblasts, which are responsible for forming some of the tissues of the oral and craniofacial complex. The hard tissue forming cells deposit a matrix composed of collagen and non-collagenous proteins (NCPs) that later undergoes mineralization. The NCPs play a role in the mineralization of collagen. One such category of NCPs is the small integrin-binding ligand, the N-linked glycoprotein (SIBLING) family of proteins. This family is composed of dentin sialophosphosprotein (DSPP), osteopontin (OPN), dentin matrix protein 1 (DMP1), bone sialoprotein (BSP), and matrix extracellular phosphoglycoprotein (MEPE). The SIBLING family is known to have regulatory effects in the mineralization process of collagen fibers and the maturation of hydroxyapatite crystals. It is well established that SIBLING proteins have critical roles in tooth development. Recent literature has described the expression and role of SIBLING proteins in other areas of the oral and craniofacial complex as well. The objective of the present literature review is to summarize and discuss the different roles the SIBLING proteins play in the development of dental, periodontal, and craniofacial tissues.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".