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Record W1983707947 · doi:10.1080/140174302762659094

No Effect of Cyclooxygenase Inhibition on Plaque Size in Atherosclerosis-prone Mice

2002· article· en· W1983707947 on OpenAlexaff
Mette Linnet Olesen, Elizabeth Kwong, Arguello Meztli, Frederic Kontny, Ingebjørg Seljeflot, Harald Arnesen, Line Graven Lyngdorf, Erling Falk

Bibliographic record

VenueScandinavian Cardiovascular Journal · 2002
Typearticle
Languageen
FieldMedicine
TopicInflammatory mediators and NSAID effects
Canadian institutionsMerck Canada Inc. (Canada)
FundersSundhed og Sygdom, Det Frie ForskningsrådMerck
KeywordsMedicineCyclooxygenaseApolipoprotein ETriglycerideApolipoprotein BInternal medicineEndocrinologySulindacCholesterolPharmacologyEnzymeBiochemistryBiologyDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: To study the role of cyclooxygenase (COX) inhibition on the development of advanced atherosclerosis in apolipoprotein E-deficient (apoE(-/-)) mice. DESIGN: Sixty apoE(-/-) mice were divided into three groups: a control group, a group fed standard mouse chow supplemented with 0.0067% (wt/wt) MF Tricyclic (selective COX-2 inhibitor), and a group fed the diet supplemented with 0.0134% (wt/wt) sulindac (non-selective COX inhibitor). Four months later, the mice were killed and the atherosclerotic plaque area in the aortic root was measured. RESULTS: Mean body weights did not differ at any time. The MF Tricyclic and sulindac groups had drug plasma levels of 1.31 +/- 0.11 and 0.84 +/- 0.23 micro g/ml, respectively. Plasma total cholesterol and triglyceride values were similar in all three groups. A small difference in plasma levels of high-density lipoprotein cholesterol was found between the groups (p = 0.03). Advanced atherosclerotic plaques were present in mice from all three groups, but there was no difference in mean plaque size between the groups (p = 0.9). CONCLUSION: Neither selective COX-2 nor non-selective COX inhibition influenced the development of advanced atherosclerosis in apoE(-/-) mice.

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)0.002
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 score0.119
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0000.000

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 head0.008
GPT teacher head0.213
Teacher spread0.204 · 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

Citations48
Published2002
Admission routes1
Has abstractyes

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