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Record W4403306060 · doi:10.3390/ijms252010897

Myeloid GSK3α Deficiency Reduces Lesional Inflammation and Neovascularization during Atherosclerotic Progression

2024· article· en· W4403306060 on OpenAlexafffund
Sarvatit Patel, Nisarg Shah, Brooke D’Mello, Anson Lee, Geoff H. Werstuck

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiomarkers in Disease Mechanisms
Canadian institutionsMcMaster UniversityUniversity of TorontoThrombosis and Atherosclerosis Research Institute
FundersUniversity of Toronto
KeywordsInflammationNeovascularizationMedicinePathologyAngiogenesisImmunologyCancer research

Abstract

fetched live from OpenAlex

The molecular mechanisms by which cardiovascular risk factors promote the development of atherosclerosis are poorly understood. We have recently shown that genetic ablation of myeloid glycogen synthase kinase (GSK)-3α attenuates atherosclerotic lesion development in low-density lipoprotein receptor-deficient (Ldlr−/−) mice. However, the precise contributions of GSK3α/β in atherogenesis are not known. The aim of this study is to investigate the effect of GSK3α and/or β deficiency on lesional inflammation and plaque vascularization. Five-week-old female Ldlr−/− mice were fed a high-fat diet for 10 weeks to establish atherosclerotic lesions. Mice were harvested at 15 weeks of age and atherosclerotic lesions were characterized. The results indicate that, in addition to significantly reducing plaque volume, GSK3α-deficiency decreases inflammation, reduces vasa vasorum density at the aortic sinus, and reduces plasma c-reactive protein (CRP) levels. GSK3β-deficiency is associated with decreased plasma CRP levels but does not affect lesional inflammation or vascularization. These results suggest GSK3α may be an applicable target for the development of novel anti-atherogenic therapies.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.269
Teacher spread0.260 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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