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Simvastatin Induces Osteogenic Differentiation and Suppresses Adipogenic Differentiation in Primarily Cultured Human Adipose-Derived Stem Cells

2009· article· en· W2109346639 on OpenAlexaff
So-Hyun Sun, Il-Kyu Lee, Jeewon Lee, Insop Shim, Se‐Hong Kim, Kyung Soo Kim

Bibliographic record

VenueBiomolecules & Therapeutics · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsMcGill University
FundersKorean Society for the Study of Obesity
KeywordsSimvastatinAdipogenesisAdipose tissueCellular differentiationCell biologyStem cellChemistryAlkaline phosphataseOil Red OVon Kossa stainEndocrinologyInternal medicineBiologyBiochemistryMedicineEnzyme

Abstract

fetched live from OpenAlex

Recent in vitro and in vivo animal studies have reported that statin, a cholesterol-lowering drug, stimulate osteogenic differentiation. In the present study, we investigated the effect of simvastatin on osteogenic and adipogenic differentiation in primarily cultured human adipose-derived stem cells (hADSCs). The simvastatin treatment significantly increased the positive cell numbers in alkaline phosphatase and von Kossa staining, and enhanced the expression levels of bone morphogenic protein (BMP)-2, core binding factor alpha 1 (cbfa1), collgen type I and osteonectin mRNAs. Lastly, hADSCs were cultured in the adipogenic media with or without simvastatin to examine the effect of simvastatin on adipogenic differentiation. In the RT-PCR analysis, there were notable decreases in mRNA expression of aP1, C/EBP-<TEX>$\alpha$</TEX> and PPAR-<TEX>$\gamma$</TEX> in hADSCs cultivated in simvastatin-added medium, compared to those in simvastatin-free medium. It suggests that the adipogenic differentiation was significantly inhibited by simvastatin treatment. These observations indicate that simvastatin induces osteogenic differentiation and suppresses adipogenic differentiation in hADSCs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.128
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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.

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

Citations5
Published2009
Admission routes1
Has abstractyes

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