MicroRNA profiling of human myeloid angiogenic cells derived from peripheral blood mononuclear cells
Bibliographic record
Abstract
Human myeloid angiogenic cells (MACs), also termed early endothelial progenitor cells, play an important role in neovascularization and vascular repair. MicroRNAs (miRNAs) are a class of naturally occurring, noncoding, short (∼22 nucleotides), single-stranded RNAs that regulate gene expression post-transcriptionally. MiRNAs have been shown to regulate MAC function. A miRNA signature of MACs was described approximately a decade ago, and many new miRNAs have been discovered in recent years. In this study, we aimed to provide an up-to-date miRNA signature for human MACs. MACs were obtained by culture of human peripheral blood mononuclear cells in endothelial medium for 7 days. Using qPCR array analysis we identified 72 highly expressed miRNAs (CT value < 30) in human MACs. RT–qPCR quantification of select miRNAs revealed a strong correlation between the CT values detected by the array analysis and RT–qPCR, suggesting the miRNA signature generated by the qPCR array assay is accurate and reliable. Experimentally validated target genes of the 10 most highly expressed miRNAs were retrieved. Only a few of the targets and their respective miRNAs have been studied for their role in MAC biology. Our study therefore provides a valuable repository of miRNAs for future exploration of miRNA function in MACs.
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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 | 0.000 |
| 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".