P767Identification of circulating miRNA-abundances in ruptured versus eroded lesions: A combined optical coherence tomography and miRNA-profiling approach in patients with acute coronary syndrome
Bibliographic record
Abstract
Background: Pathophysiologic mechanisms of ACS caused by plaque rupture (ACS with ruptured fibrous cap = RFC-ACS) and plaque erosion (ACS with intact fibrous cap = IFC-ACS) are still poorly understood. Experimental studies indicate that microRNAs orchestrate pathophysiological pathways involved in plaque instability. Therefore, we assessed whether abundances of circulating miRNAs (miRNAs) differ locally at culprit sites of patients presenting with RFC-ACS and IFC-ACS. Methods and results: Patients with ACS were consecutively enrolled. Blood samples were aspirated locally at the area of culprit lesion (local) and systemically from the arterial sheath (systemic). The ACS-causing culprit lesion was assessed by optical coherence tomography (OCT) and classified as RFC-ACS or IFC-ACS. In addition, systemic and local coronary blood was sampled in patients with stable coronary artery disease (CAD). Patients with RFC-ACS, IFC-ACS and CAD were matched 1:1:1 according to gender, age, diabetes and hypertension (n=15 per group). Using Platelet-poor plasma, we compared 179 circulating miRNAs after normalization based on lower quartile of Ct-values per sample. We used a linear model adjusting for body mass index (BMI), TIMI-flow and RNA spike-in (Ct UniSp4 – UniSp2)) to assess differentially abundant miRNAs.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".