microRNA signatures of perioperative myocardial injury after elective non-cardiac surgery: prospective observational cohort study
Bibliographic record
Abstract
Background Elevated plasma/serum troponin, indicating perioperative myocardial injury (PMI), is common after non-cardiac surgery. However, underlying mechanisms remain unclear. Acute coronary syndrome (ACS) is associated with the early appearance of circulating microRNAs, which regulate post-translational gene expression. We hypothesised that if PMI and ACS share pathophysiological mechanisms, common microRNA signatures should be evident. Methods Nested case-control study of samples obtained before and after non-cardiac surgery from patients enrolled in two prospective observational studies of PMI (postoperative troponin I/T>99 th centile). In cohort one, serum microRNAs were compared between patients with/without PMI, matched for age, gender and comorbidity. Real-time polymerase chain reaction quantified relative microRNA expression (cycle quantification threshold <37) before and after surgery for microRNA signatures associated with ACS, blinded to PMI. In cohort two, we analysed (EdgeR) microRNA from plasma extracellular vesicles using next-generation sequencing (Illumina HiSeq500). microRNA-messenger RNA-function pathway analysis was performed (DIANA miRPath v3.0/TopGO). Results MicroRNA were detectable in all 59 patients (median age:67yrs (61-75); 42% male), who had similar clinical characteristics independent of developing PMI. In cohort one, PMI was not associated with increased serum microRNA expression levels after surgery (hsa-miR-1-3p mean fold-change (FC):3.99 (95%CI:1.95-8.19); hsa-miR-133-3p FC:5.67(95%CI:2.94-10.91); p<0.001). hsa-miR-208b-3p was more commonly detected after PMI (odd ratio (OR):10.0 (95%CI:1.9-52.6); p<0.01). Bioinformatic analysis of differentially expressed microRNAs from cohorts one and two identified pathways associated with adrenergic stress involving calcium dysregulation, rather than ischaemia. Conclusions Circulating microRNAs synonymous with cardiac ischaemia were universally elevated in patients after surgery, independent of developing myocardial injury.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".