Extracellular communication via microRNA: lipid particles have a new message
Bibliographic record
Abstract
The complexity of microRNA (miRNA)-mediated pathway control has burgeoned since the discovery that miRNAs are found in the extracellular space and constitute a form of cell-cell communication. miRNAs have been found in plasma, urine, and saliva and have recently been shown to be carried on lipoproteins. This has led to the proposal that circulating miRNAs may be useful biomarkers of various diseases, including cardiovascular disease, diabetes, and other forms of dysregulated metabolism. Although our understanding of the cellular machinery responsible for the secretion of miRNA is incomplete, it has been demonstrated that miRNAs are packaged into exosomes, microvesicles, and apoptotic bodies by a broad range of cell types. Intriguingly, a large portion of extracellular miRNA is found outside of any lipid-containing vesicle, and instead is associated with RNA binding proteins like argonautes 1 and 2, which may aid in their protection from abundant nucleases in the extracellular space. The excitement for miRNAs as biomarkers is mounting as more and more evidence supports that these noncoding RNAs are actively secreted from diseased tissues, possibly before the onset of overt disease. While caution should be taken in these early days, there is little doubt that extracellular miRNAs will hold tremendous potential as both diagnostic and therapeutic agents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".