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Record W2002328868 · doi:10.1155/2008/813236

CD36 Is Significantly Correlated with Adipophilin in Human Carotid Lesions and Inversely Correlated with Plasma ApoAI

2008· article· en· W2002328868 on OpenAlexfundno aff
Sophie Collot-Teixeira, Calypso Barbatis, F. Bultelle, Michael Koutouzis, Gerard Pasterkamp, Paul Fraser, Zenon S. Kyriakides, Robin N. Poston, Angelique Ristagno, Lilian McGregor, Chantal M. Boulanger, Guy Lesèche, John L. McGregor

Bibliographic record

VenueBioMed Research International · 2008
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsnot available
FundersInstitut National de la Santé et de la Recherche MédicaleBritish Heart FoundationKing's College LondonW. Garfield Weston Foundation
KeywordsCD36Internal medicineMedicineEndocrinologyCardiologyPathologyBiology

Abstract

fetched live from OpenAlex

OxLDL uptake and cholesterol efflux inhibition in macrophages play a key role in atherosclerotic plaque formation, rupture, and thrombotic ischemia. This study investigates genes implicated in OxLDL uptake (CD36, SRA), cholesterol efflux inhibition (adipophilin, ADFP), and inflammatory recruitments of leukocytes (IL-8) in plaque lesion areas (PLAs) compared to nonplaque lesion areas (NPLAs) in human carotid endarterectomy specimens. Gene and protein expressions were assayed using quantitative PCR and quantitative immunohistochemistry. Pearson tests were used to investigate potential correlation between (a) different gene expressions and (b) gene expression and patient's plasma constituents. CD36, SRA, ADFP, and IL-8 were shown to be significantly more expressed in PLA compared to NPLA. In PLA, a significant correlation was observed between CD36, SRA, ADFP, and IL-8 mRNA levels. Moreover, CD36 expression level was significantly inversely correlated to plasma marker ApoAI. The above investigated genes/proteins may play a key role in the maturation of atherosclerotic lesions.

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 score0.191
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.071
GPT teacher head0.325
Teacher spread0.254 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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