MicroRNA-146a: A Dominant, Negative Regulator of the Innate Immune Response
Bibliographic record
Abstract
MicroRNAs (miRNAs) are a class of small non-coding RNA molecules that can play critical roles as regulators of numerous pathways and biological processes including the immune response. Emerging as one of the most important miRNAs to orchestrate immune and inflammatory signaling, often through its recognized target genes, IRAK1 and TRAF6, is microRNA-146a (miR-146a). MiR-146a is one, of a small number of miRNAs, whose expression is strongly induced following challenge of cells with bacterial endotoxin, and prolonged expression has been linked to immune tolerance, implying that it acts as a fine-tuning mechanism to prevent an overstimulation of the inflammatory response. In other cells, miR-146a has been shown to play a role in the control of the differentiation of megakaryocytic and monocytic lineages, adaptive immunity, and cancer. In this review, we discuss the central role prescribed to miR-146a in innate immunity. We particularly focus on the role played by miR-146a in the regulation and signaling mediated by one of the main pattern recognition receptors, toll/IL-1 receptors (TLRs). Additionally, we also discuss the role of miR-146a in several classes of autoimmune pathologies where this miRNA has been shown to be dysregulated, as well as its potential role in the pathobiology of neurodegenerative diseases.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".