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Record W2061077119 · doi:10.1139/y04-112

The pro-oxidant role of methylglyoxal in mesenteric artery smooth muscle cells

2005· article· en· W2061077119 on OpenAlexafffundvenue
Lingyun Wu

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Glycation End Products research
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsMethylglyoxalChemistrySmooth muscleInternal medicineCell biologyBiochemistryEndocrinologyAnatomyMedicineBiologyEnzyme

Abstract

fetched live from OpenAlex

Methylglyoxal (MG), a highly reactive metabolite of glucose, causes non-enzymatic glycation of proteins to form irreversible advanced glycation endproducts (AGEs). The present study investigated whether methylglyoxal induced oxidative stress and activated nuclear factor kappa B (NF-kappaB) in freshly isolated and cultured smooth muscle cells (SMCs) from rat mesenteric artery. The treatment of cells with MG (50 or 100 micromol/L) induced a significant increase in AGE formation and oxidation of DCF. MG-enhanced generation of AGEs and the oxidation of DCF was markedly inhibited by antioxidant n-acetylcysteine (NAC, 600 micromol/L). MG at a concentration of 100 micromol/L increased the heme-oxygenase-1 expression in these cells. Moreover, MG activated NF-kappaB p65, indicated by an increased immuno cytochemistry stain for NF-kappaB p65 located in the nucleus after the treatment of mesenteric artery SMCs with MG. MG-induced activation of NF-kappaB p65 was inhibited by NAC. In summary, MG significantly increases oxidative stress and activates NF-kappaB p65 in mesenteric artery SMCs. The pro-oxidant role of methylglyoxal may contribute to various pathological changes of SMCs from resistance arteries.

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 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 score0.161
Threshold uncertainty score0.243

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 head0.010
GPT teacher head0.287
Teacher spread0.277 · 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

Citations50
Published2005
Admission routes3
Has abstractyes

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