A meta-analysis of the relationship between circulating microRNA-155 and coronary artery disease
Bibliographic record
Abstract
OBJECTIVE: Coronary artery disease (CAD) is a leading cause of death worldwide. Many studies in China and abroad have reported an association between the expression level of microRNA-155 and CAD; however, the results remain controversial. We aimed to comprehensively investigate this association based on a meta-analysis. METHODS: We first systematically searched eight Chinese and English databases, including China National Knowledge Infrastructure, Wanfang, China Science and Technology Journal Database, PubMed, Web of Science, Embase, Google Scholar, and Cochrane Library, to identify studies concerning the relationship between microRNA-155 levels and CAD published before February 7, 2021. The quality of the literature was assessed by the Newcastle-Ottawa Scale (NOS). Meta-analysis was performed using a random-effects model to calculate the standard mean difference with a 95% confidence interval (CI). RESULTS: Sixteen articles with a total of 2069 patients with CAD and 1338 controls were included. All the articles were of high quality according to the NOS. The meta-analysis showed that the mean level of microRNA-155 was significantly lower in patients with CAD than in controls. Based on subgroup analyses, the level of microRNA-155 in the plasma of CAD patients and in acute myocardial infarction (AMI) patients was significantly lower than that in controls, whereas this level in CAD patients with mild stenosis was significantly higher than that in controls. CONCLUSION: Our study indicates that the expression level of circulating microRNA-155 in patients with CAD is lower than that in a non-CAD group, suggesting a new possible reference index for the diagnosis and monitoring of patients with CAD.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.056 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".