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The location and characteristics of two populations of dental pulp cells affect tooth development

2009· article· en· W2017185409 on OpenAlexaff
Yoshinori Sumita, Shuhei Tsuchiya, Izumi Asahina, Hideaki Kagami, Masaki Honda

Bibliographic record

VenueEuropean Journal Of Oral Sciences · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicdental development and anomalies
Canadian institutionsMcGill University
Fundersnot available
KeywordsDentistryAffect (linguistics)Pulp (tooth)OrthodonticsMedicinePsychologyCommunication

Abstract

fetched live from OpenAlex

This study investigated the characteristics of two dental pulp cell populations during the early stages of crown formation in porcine teeth. A transplantation method was developed to reproduce epithelial cell-mesenchymal cell interactions during odontogenesis (tooth development). The technique allowed two types of cells/tissue to be combined in vivo. Populations of cells localized in the cervical loop epithelium region, dental pulp horn, and dental pulp core chambers were isolated and dissociated into single cells. Each population was examined for its gene-expression pattern using both semiquantitative and quantitative reverse transcription-polymerase chain reaction (RT-PCR) analyses, and for its tissue-formation capability by combining the cervical loop epithelial cells with either pulp horn cells or pulp core cells on biodegradable collagen scaffolds that were subsequently examined using histology and immunohistology. Gene-expression patterns showed that pulp horn cells were more mature than pulp core cells. Cervical loop epithelial cells combined with pulp horn cells mainly reconstituted dentin-cementum structures. By contrast, cervical loop epithelial cells combined with pulp core cells reconstituted enamel-dentin structures. These results suggest that mesenchymal cells residing in a specific location of the pulp possess a specific tissue-formation potential when combined with epithelial cells.

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 score0.686
Threshold uncertainty score0.113

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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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

Citations19
Published2009
Admission routes1
Has abstractyes

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