Recent advances in proteomic analysis to study carotid artery plaques
Bibliographic record
Abstract
Objective: We sought to identify differentially expressed proteins in serum, plasma, and plaque samples of patients with carotid atherosclerotic lesions. Methods: We performed a systematic review of the proteomic profile of serum, plasma, and plaque samples of patients with carotid artery disease. We included full-length peer-reviewed studies of adult humans and reported them using PRISMA guidelines. The quality of the design and content of the articles included in the review was assessed using the Newcastle-Ottawa scale. Results: We included six peer-reviewed articles reporting protein expression in serum, plasma, or plaque samples from patients with carotid atherosclerosis. Three were single-center cross-sectional studies, two were single-center case-control studies, and one was a single-center cohort study. Thirty-six proteins were found to be expressed differentially when comparing samples from healthy subjects and individuals with diseased carotid vessels and between patients with symptomatic and asymptomatic carotid artery atherosclerotic lesions. Some of these were shown to be related to inflammatory or anti-inflammatory pathways in atherogenesis. CD5L and S100A12 were both found to be upregulated in patients with unstable plaque, the former owing to its anti-inflammatory properties and the latter for its pro-oxidant effects in atherosclerosis. ACTB is involved in cellular structure and integrity and was found to be downregulated in patients with ruptured carotid plaques. Conclusions: Atherosclerotic carotid disease places the patient at increased risk of ischemic neurological events. Proteomics may help to understand their pathophysiological processes and can identify differential protein expression in blood samples from healthy subjects and patients with carotid artery plaques. This patient-centered approach will allow for the timely identification of individuals at higher risk of experiencing stroke.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".