PCSK9 expression in fibrous cap possesses a marker for rupture in advanced plaque
Bibliographic record
Abstract
BACKGROUND: To date, PCSK9 inhibitors are well known for eliminating cardiac and cerebral artery ischemia events by lowering the serum lipid level. However, the pathophysiological value of in-plaque PCSK9 expression is still unclear. METHODS: Advanced plaques removed by carotid endarterectomy were sectioned and stained to identify the PCSK9 expression pattern and its co-expression with rupture-relevant markers. To investigate the correlation of PCSK9 expression with regional blood shear flow, hemodynamic characteristics were analyzed using computational fluid dynamics, and representative parameters were compared between PCSK9 positive and negative staining plaques. To explore this phenomenon in vitro, human aortic vascular smooth muscle cells were used to overexpress and knock down PCSK9. The impacts of PCSK9 modulations on mechanical sensor activity were testified by western blot and immunofluorescence. Real-time polymerase chain reaction was used to evaluate the transcription levels of downstream rupture-prone effectors. RESULTS: PCSK9 distribution in plaque preferred cap and shoulder regions, residing predominantly in smooth muscle actin-positive cells. Cap PCSK9 expression correlated with fibrous cap thickness negatively and co-expressed with MMP-9, both pointing to the direction of plaque rupture. A hemodynamic profile indicated a rupture-prone feature of cap PCSK9 expression. In vitro, overexpression and knockdown of PCSK9 in human aortic vascular smooth muscle cells has positive modulation on mechanical sensor Yes-associated protein 1 (YAP) activity and transcription levels of its downstream rupture-prone effectors. Serial section staining verified in situ colocalization among PCSK9, YAP, and downstream effectors. CONCLUSIONS: Cap PCSK9 possesses a biomarker for rupture risk, and its modulation may lead to a novel biomechanical angle for plaque interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".