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Record W4283463923 · doi:10.3389/fdmed.2022.898802

The Role of SIBLING Proteins in Dental, Periodontal, and Craniofacial Development

2022· article· en· W4283463923 on OpenAlexaff
Carlos Alberto Figueredo, Nancy Abdelhay, Monica Prasad Gibson

Bibliographic record

VenueFrontiers in Dental Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCraniofacialSiblingMedicineDentistryOrthodonticsBiologyGeneticsPsychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.240
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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