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Record W1977340750 · doi:10.1093/toxsci/kfr097

Exposure to Moderate Arsenic Concentrations Increases Atherosclerosis in ApoE−/− Mouse Model

2011· article· en· W1977340750 on OpenAlexafffund
Maryse Lemaire, Catherine A. Lemarié, Manuel Flores Molina, Ernesto L. Schiffrin, Stéphanie Lehoux, Koren K. Mann

Bibliographic record

VenueToxicological Sciences · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicArsenic contamination and mitigation
Canadian institutionsMcGill UniversityJewish General Hospital
FundersNational Institute of Environmental Health SciencesCanadian Institutes of Health ResearchNational Institutes of HealthMcGill University
KeywordsArsenicProinflammatory cytokineIn vivoChemistryMedicineInflammationPhysiologyBiologyEndocrinologyInternal medicineBiotechnology

Abstract

fetched live from OpenAlex

Arsenic is a widespread environmental contaminant to which millions of people are exposed worldwide. Exposure to arsenic is epidemiologically linked to increased cardiovascular disease, such as atherosclerosis. However, the effects of moderate concentrations of arsenic on atherosclerosis formation are unknown. Therefore, we utilized an in vivo ApoE(-/-) mouse model to assess the effects of chronic moderate exposure to arsenic on plaque formation and composition in order to facilitate mechanistic investigations. Mice exposed to 200 ppb arsenic developed atherosclerotic lesions, a lower exposure than previously reported. In addition, arsenic modified the plaque content, rendering them potentially less stable and consequently, potentially more dangerous. Moreover, we observed that the lower exposure concentration was more atherogenic than the higher concentration. Arsenic-enhanced lesions correlated with several proatherogenic molecular changes, including decreased liver X receptor (LXR) target gene expression and increased proinflammatory cytokines. Significantly, our observations suggest that chronic moderate arsenic exposure may be a greater cardiovascular health risk than previously anticipated.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.998

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)0.0030.000

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 head0.080
GPT teacher head0.264
Teacher spread0.184 · 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 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

Citations72
Published2011
Admission routes2
Has abstractyes

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