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Record W2084468220 · doi:10.1155/2011/164197

AP-1 as a Regulator of MMP-13 in the Stromal Cell of Giant Cell Tumor of Bone

2011· article· en· W2084468220 on OpenAlexafffund
Isabella W.Y. Mak, Robert Turcotte, Snežana Popović, Gurmit Singh, Michelle Ghert

Bibliographic record

VenueBiochemistry Research International · 2011
Typearticle
Languageen
FieldMedicine
TopicBone Tumor Diagnosis and Treatments
Canadian institutionsMcGill University Health CentreMontreal General HospitalMcMaster UniversityHamilton Health SciencesJuravinski Cancer Centre
FundersCanadian Institutes of Health ResearchMcMaster University
KeywordsStromal cellRUNX2Transcription factorGene knockdownMatrix metalloproteinaseCancer researchStromal cell-derived factor 1CellGiant-cell tumor of boneCell biologyRegulatorBiologyCell cultureMedicinePathologyImmunologyGeneChemokineCXCR4InflammationGiant cellGenetics

Abstract

fetched live from OpenAlex

Matrix-metalloproteinase-13 (MMP-13) has been shown to be an important protease in inflammatory and neoplastic conditions of the skeletal system. In particular, the stromal cells of giant cell tumor of bone (GCT) express very high levels of MMP-13 in response to the cytokine-rich environment of the tumor. We have previously shown that MMP-13 expression in these cells is regulated, at least in part, by the RUNX2 transcription factor. In the current study, we identify the expression of the c-Fos and c-Jun elements of the AP-1 transcription factor in these cells by protein screening assays and real-time PCR. We then used siRNA gene knockdown to determine that these elements, in particular c-Jun, are upstream regulators of MMP-13 expression and activity in GCT stromal cells. We conclude that there was no synergy found between RUNX2 and AP-1 in the regulation of the MMP13 expression and that these transcription factors may be independently regulated in these 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.066
GPT teacher head0.348
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2011
Admission routes2
Has abstractyes

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Same venueBiochemistry Research InternationalSame topicBone Tumor Diagnosis and TreatmentsFrench-language works237,207