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Record W3009415160 · doi:10.1101/2020.02.24.20027383

microRNA signatures of perioperative myocardial injury after elective non-cardiac surgery: prospective observational cohort study

2020· preprint· en· W3009415160 on OpenAlexaff
Shaun M. May, Tom Abbott, Ana Gutierrez del Arroyo, Anna Reyes, Gladys Martir, Robert Stephens, David Brealey, Brian H. Cuthbertson, Duminda N. Wijeysundera, Rupert M. Pearse, Gareth L. Ackland

Bibliographic record

VenuemedRxiv · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
FundersMedical Research Council
KeywordsMedicinePerioperativeAcute coronary syndromemicroRNACohortTroponinProspective cohort studyInternal medicineCohort studyBiomarkerTroponin ITroponin TCardiologySurgeryMyocardial infarctionGeneBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.263
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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