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Consequences for enamel development and mineralization resulting from loss of function of ameloblastin or enamelin

2009· article· en· W2109772938 on OpenAlexafffund
Charles E. Smith, Rima Wazen, Yuanyuan Hu, S. Zalzal, Antonio Nanci, James P. Simmer, Jan C.‐C. Hu

Bibliographic record

VenueEuropean Journal Of Oral Sciences · 2009
Typearticle
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsUniversité de Montréal
FundersNational Institute of Dental and Craniofacial ResearchCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsEnamel paintAmeloblastMineralization (soil science)AmelogeninChemistryMolarAmelogenesisDentistryAnatomyCell biologyBiologyMedicine

Abstract

fetched live from OpenAlex

Although the nonamelogenin proteins, ameloblastin and enamelin, are both low-abundance and rapidly degrading components of forming enamel, they seem to serve essential developmental functions, as suggested by findings that an enamel layer fails to appear on teeth of mice genetically engineered to produce either a truncated form of ameloblastin (exons 5 and 6 deleted) or no enamelin at all (null). The purpose of this study was to characterize, by direct micro weighing, changes in enamel mineralization occurring on maxillary and mandibular incisors of mice bred for these alterations in nonamelogenin function (Ambn(+/+, +/-5,6, -5,6/-5,6), Enam(+/+, +/- ,-/-)). The results indicated similar changes to enamel-mineralization patterns within the altered genotypes, including significant decreases by as much as 50% in the mineral content of maturing enamel from heterozygous mice and the formation of a thin, crusty, and disorganized mineralized layer, rather than true enamel, on the labial (occlusal) surfaces of incisors and molars along with ectopic calcifications within enamel organ cells in Ambn(-5,6/-5,6) and Enam(-/-) homozygous mice. These findings confirm that both ameloblastin and enamelin are required by ameloblasts to create an enamel layer by appositional growth as well as to assist in achieving its unique high level of mineralization.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.150

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 head0.075
GPT teacher head0.311
Teacher spread0.236 · 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 designBench or experimental
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

Citations63
Published2009
Admission routes2
Has abstractyes

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