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Immunocytochemical characterization of ectopic enamel deposits and cementicles in human teeth

2003· article· en· W2108647172 on OpenAlexaff
Dieter D. Bosshardt, Antonio Nanci

Bibliographic record

VenueEuropean Journal Of Oral Sciences · 2003
Typearticle
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsEnamel paintOsteopontinCementumChemistryAmelogeninBone sialoproteinMineralization (soil science)Ground substancePeriodontal fiberCementogenesisDental follicleMatrix (chemical analysis)AnatomyPathologyAlkaline phosphataseDentistryOsteocalcinDentinConnective tissueBiologyMedicineInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Despite the relative frequency and clinical relevance of radicular enamel deposits and cementicles, their etiology and nature are unknown. The purpose of the present study was therefore to evaluate the presence and distribution of mineralization-associated non-collagenous matrix proteins (NCPs) in various types of root-associated ectopic mineralizations. Human teeth were processed for embedding in epoxy or acrylic resins. Tissue sections were incubated with antibodies to amelogenins (AMEL), bone sialoprotein (BSP), and osteopontin (OPN). Radicular enamel deposits contained residual organic matrix that labeled for AMEL. In contrast, BSP and OPN were not detected in the residual enamel matrix, they were found in the cementum deposited on its surface as well as in collagen-free cementicle-like structures in the adjacent periodontal ligament. True cementicles consisted of a collagenous matrix intermixed with a non-collagenous ground substance. Labeling for BSP and OPN was mainly associated with the interfibrillar ground substance. No immunoreactivity for AMEL was detected in cementicles. These data indicate that ectopic enamel deposits on the root retain a high amount of AMEL, whereas cementicles contain BSP and OPN, two NCPs typically found in bone and cementum. These NCPs may, like in their normal tissue counterparts, play a role in the mineralization process.

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 score0.644
Threshold uncertainty score0.117

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 head0.038
GPT teacher head0.294
Teacher spread0.256 · 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

Citations16
Published2003
Admission routes1
Has abstractyes

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