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Record W3163686766 · doi:10.1161/strokeaha.120.030228

Expression of Resistin, Chemerin, and Chemerin’s Receptor in the Unstable Carotid Atherosclerotic Plaque

2021· article· en· W3163686766 on OpenAlexafffund
Russell Yanofsky, Karina Gasbarrino, Huaien Zheng, Robert J. Doonan, Fanny Jaunet, Samantha Steinmetz-Wood, John P. Veinot, Chi Lai, Stella S. Daskalopoulou

Bibliographic record

VenueStroke · 2021
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsUniversity of OttawaMcGill University Health CentreUniversité de MontréalMcGill University
FundersCanadian Institutes of Health Research
KeywordsResistinChemerinAdipokineMedicineInternal medicineEndocrinologyPathologyLeptin

Abstract

fetched live from OpenAlex

Background and Purpose: Unstable carotid plaques are a common cause of ischemic strokes. Identifying markers that reflect/contribute to plaque instability has become a prominent focus in cardiovascular research. The adipokines, resistin and chemerin, and ChemR23 (chemerin receptor), may play a role in carotid atherosclerosis, making them potential candidates to assess plaque instability. However, the expression and interrelationship of resistin and chemerin (and ChemR23) protein and mRNA within the carotid atherosclerotic plaque remains elusive. Thus, we investigated herein, the association between plaque mRNA and protein expression of resistin and chemerin (and ChemR23) and carotid plaque instability in humans, and whether sex differences exist in the relationship between these adipokines and plaque instability. Methods: Human carotid plaques were processed for immunohistochemical/mRNA analysis of resistin, chemerin, and ChemR23. Plaque instability was assessed by gold-standard histological classifications. A semi-quantitative scoring system was used to determine the intensity of adipokine expression on macrophages/foam cells, as well as the percentage of inflammatory cells stained positive. Plaque adipokine protein expression was also digitally quantified and mRNA expression was assessed by qRT-PCR. Results: Resistin and chemerin mRNA expression was 80% and 32% lower, respectively, in unstable versus stable plaques (P<0.05), while no difference in ChemR23 mRNA expression was observed. In contrast, greater resistin staining intensity and percentage of cells stained positive were detected in unstable versus stable plaques (P<0.01). Similarly, chemerin and ChemR23 staining intensity and percentage of cells stained were positively associated with plaque instability (P<0.05). No strong sex-specific relationship was observed between adipokines and plaque instability. Conclusions: This study examined the relationship between resistin, chemerin, and ChemR23, and carotid plaque instability, with a specific analysis at the plaque level. We reported a positive association between plaque instability and protein levels of resistin, chemerin, and ChemR23 but a negative association with resistin and chemerin mRNA expression. This suggests these adipokines exert proinflammatory roles in the process of carotid atherosclerosis and may be regulated via a negative feedback regulatory mechanism.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2021
Admission routes2
Has abstractyes

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