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Relationships between protein and mineral during enamel development in normal and genetically altered mice

2011· article· en· W1894737770 on OpenAlexafffund
Charles E. Smith, Yuanyuan Hu, Amelia S. Richardson, John D. Bartlett, Jan C.‐C. Hu, James P. Simmer

Bibliographic record

VenueEuropean Journal Of Oral Sciences · 2011
Typearticle
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsMcGill University
FundersNational Institute of Dental and Craniofacial ResearchCanadian Institutes of Health ResearchNational Institutes of HealthU.S. Public Health ServiceMcGill University
KeywordsEnamel paintChemistryAmelogeninMineralization (soil science)KallikreinIncisorInternal medicineBiochemistryDentistryEnzymeGene

Abstract

fetched live from OpenAlex

The purpose of this study was to quantify and compare the amounts of volatiles (mostly protein) and mineral present in developing incisor enamel in normal mice and in those genetically engineered for absence of intact enamelin, ameloblastin, matrix metalloproteinase 20 (MMP20) or kallikrein-related peptidase 4 (KLK4). Data indicated that all mice showed peaks in the gross weight of volatiles and a similar weight of mineral at locations on incisors normally associated with early maturation. Thereafter, the content of volatiles on normal incisors declined rapidly by as much as 62%, but not by 100%, over 2 mm, accompanied by increases of ≈ threefold in mineral weights. Enamelin heterozygous mice (lower incisors) showed a decrease in volatile content across the maturation stage, yet mineral failed to increase significantly. Mmp20 null mice showed no significant loss of volatiles from maturing enamel, yet the amount of mineral increased. Klk4 null mice showed normal mineral acquisition up to early maturation, but the input of new volatiles in mid to late maturation caused the final mineralization to slow below normal levels. These results suggest that it is not only the amount of protein but also the nature or type of protein or fragments present in the local crystallite environment that affects their volumetric expansion as they mature.

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 score0.028
Threshold uncertainty score0.176

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 head0.106
GPT teacher head0.270
Teacher spread0.164 · 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

Citations40
Published2011
Admission routes2
Has abstractyes

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