MiR-494-3p alleviates acute lung injury through regulating NLRP3 activation by targeting CMPK2
Bibliographic record
Abstract
Acute lung injury (ALI) is a severe respiratory disorder with a high rate of mortality, and is characterized by excessive cell apoptosis and inflammation. MicroRNAs (miRNAs) play pivotal roles in ALI. This study examined the biological function of miR-494-3p in cell apoptosis and inflammatory response in ALI. For this, mice were injected with lipopolysaccharide (LPS) to generate an in-vivo model of ALI (ALI mice), and WI-38 cells were stimulated with lipopolysaccharide (LPS) to generate an in-vitro model of ALI. We found that miR-494-3p was significantly downregulated in the ALI mice and in the in-vitro model. Overexpression of miR-494-3p inhibited inflammation and cell apoptosis in the LPS-induced WI-38 cells, and improved the symptoms of lung injury in the ALI mice. We then identified cytidine/uridine monophosphate kinase 2 (CMPK2) as a novel target of miR-494-3p in the WI-38 cells. Furthermore, miR-494-3p suppressed cell apoptosis and the inflammatory response in LPS-treated WI-38 cells through targeting CMPK2. The NLRP3 inflammasome is reportedly responsible for the activation of inflammatory processes. In our study, CMPK2 was confirmed to activate the NLRP3 inflammasome in LPS-treated WI-38 cells. In conclusion, miR-494-3p attenuates ALI through inhibiting cell apoptosis and the inflammatory response by targeting CMPK2, which suggests the value of miR-494-3p as a target for the treatment for ALI.
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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 | 0.001 |
| 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".