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Record W2572801083 · doi:10.4317/jced.52824

Growth kinetics and characterization of human dental pulp stem cells: Comparison between third molar and first premolar teeth

2017· article· en· W2572801083 on OpenAlexaff
Davood Mehrabani, P Mahdiyar, Kianoosh Torabi, Reza Robati, S Zare, Mehdi Dianatpour, Amin Tamadon

Bibliographic record

VenueJournal of Clinical and Experimental Dentistry · 2017
Typearticle
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsUniversity of Manitoba
FundersShiraz UniversityShiraz University of Medical SciencesIslamic Azad University
KeywordsDental pulp stem cellsMolarPremolarPulp (tooth)CD90DentistryChemistryMesenchymal stem cellCD44Stem cellTissue engineeringCell biologyBiologyCD34Biomedical engineeringCellMedicineBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Dental pulp stem cells (DPSCs) play an important role in tissue regeneration. This study compares the growth kinetics and characterization of third molar and first premolar human DPSCs. MATERIAL AND METHODS: Dental pulp tissues were isolated from human first premolar and third molar teeth and were digested by treating them with collagenase type I. Single-cell suspensions from each dental pulp were seeded in T25 culture flasks and the media were replaced every 3 days until 70% confluence. The cells were enumerated to determine the population doubling time (PDT). Cells were characterized using flow cytometry, RT-PCR and osteogenic medium for differentiation of DPSCs. Karyotyping assay was also performed till passage 7th. RESULTS: The DPSCs had spindle-shaped morphology. There was an increase in PDT in third molar DPSCs when compared to first premolar teeth. Positive expression of CD44, CD73, and CD90 and negative expression of CD34 and CD45 were illustrated. A normal karyotype was visible for all seven passages. The Alizarin red staining was positive for osteogenic induction of DPSCs. CONCLUSIONS: Stem cells, dental pulp, growth kinetics, characterization.

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 score0.042
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.116
GPT teacher head0.437
Teacher spread0.321 · 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

Citations33
Published2017
Admission routes1
Has abstractyes

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