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Record W3210479445 · doi:10.1016/j.jsb.2021.107805

A genetic model for the secretory stage of dental enamel formation

2021· review· en· W3210479445 on OpenAlexaff
James P. Simmer, Jan C.‐C. Hu, Yuanyuan Hu, Shelly Zhang, Tian Liang, Shih‐Kai Wang, Jung‐Wook Kim, Yasuo Yamakoshi, Yong‐Hee Patricia Chun, John D. Bartlett, Charles E. Smith

Bibliographic record

VenueJournal of Structural Biology · 2021
Typereview
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsMcGill University
FundersNational Institute of Dental and Craniofacial ResearchNational Institutes of HealthNational Taiwan University HospitalMinistry of Science and Technology, TaiwanNational Research Foundation of Korea
KeywordsAmeloblastEnamel paintAmelogenesisDentinCell biologyEnamel organUltrastructureChemistryBiologyDentistryAnatomyMedicine

Abstract

fetched live from OpenAlex

The revolution in genetics has rapidly increased our knowledge of human and mouse genes that are critical for the formation of dental enamel and helps us understand how enamel evolved. In this graphical review we focus on the roles of 41 genes that are essential for the secretory stage of amelogenesis when characteristic enamel mineral ribbons initiate on dentin and elongate to expand the enamel layer to the future surface of the tooth. Based upon ultrastructural analyses of genetically modified mice, we propose a molecular model explaining how a cell attachment apparatus including collagen 17, α6ß4 and αvß6 integrins, laminin 332, and secreted enamel proteins could attach to individual enamel mineral ribbons and mold their cross-sectional dimensions as they simultaneously elongate and orient them in the direction of the retrograde movement of the ameloblast membrane.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.080
GPT teacher head0.380
Teacher spread0.300 · 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 designOther design
Domainnot available
GenreReview

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

Citations46
Published2021
Admission routes1
Has abstractyes

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