Lipidomic Predictors of Coronary No-Reflow
Bibliographic record
Abstract
The ‘no-reflow’ phenomenon (NRP) after primary percutaneous coronary intervention (PCI) is a serious complication among acute ST-segment elevation myocardial infarction (STEMI) patients. Herein, a comprehensive lipidomics approach was used to quantify over 300 distinct molecular species in circulating plasma from 126 patients with STEMI before and after primary PCI. Our analysis showed that three lipid classes: phosphatidylcholine (PC), alkylphosphatidylcholine (PC(O)), and sphingomyelin (SM), were significantly elevated (p < 0.05) in no-reflow patients before primary PCI. The levels of individual fatty acids and total fatty acid levels were significantly lower (p < 0.05) in no-reflow subjects after PCI. The grouping of patients based on ECG ST-segment resolution (STR) also demonstrated the same trend, confirming the possible role of these differential lipids in the setting of no-reflow. Sphingomyelin species, SM 41:1 and SM 41:2, was invariably positively correlated with corrected TIMI frame count (CTFC) at pre-PCI and post-PCI. The plasma levels of SM 42:1 exhibited an inverse association (p < 0.05) consistently with tumor necrosis factor-alpha (TNF-α) at pre-PCI and post-PCI. In conclusion, we identified plasma lipid profiles that distinguish individuals at risk of no-reflow and provided novel insights into how dyslipidemia may contribute to NRP after primary PCI.
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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 | 0.000 | 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 | 0.000 |
| 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".