The role of microRNA-146a in regulating the expression of IRAK1 in cerebral ischemia–reperfusion injury
Bibliographic record
Abstract
MicroRNA-146a (miR-146a) is reportedly implicated in the pathogenesis of ischemia-reperfusion (I/R) injury; however, its role in cerebral I/R injury is unclear and requires further investigation. In this study, cerebral I/R injury was established in mice via middle cerebral artery occlusion, and the expression of miR-146a was detected in the brain tissue via quantitative real-time PCR. We found that the expression of miR-146a was upregulated. Furthermore, the endogenous miR-146a was antagonized by its specific inhibitor. The results indicated that the inhibition of miR-146a deteriorated I/R-induced neurobehavioral impairment, exaggerated the infarct size, and exacerbated blood-brain barrier leakage. Cerebral I/R injury-induced generation of inflammatory cytokines, tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6, was further promoted by miR-146a inhibitor. The expression of interleukin-1 receptor associated kinase 1 (IRAK1), a target of miR-146a, was upregulated upon miR-146a inhibition. In addition, the nuclear factor κB (NF-κB) signaling pathway was over-activated when miR-146a was antagonized as manifested by the increased levels of phospho-NF-κB inhibitor α and nuclear p65. In summary, our findings demonstrate that the elevation of miR-146a may be one of the compensatory responses after the cerebral I/R injury and suggest miR-146a as a potential therapeutic target for cerebral I/R injury.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".