MicroRNAs are dynamically regulated and play an important role in LPS-induced lung injury
Bibliographic record
Abstract
Acute lung injury is characterized by an increase of inflammatory reaction and severe lung edema. Even if there have been great advances in the identification of genes and signaling pathways involved in acute lung injury, the fundamental mechanisms of initiation and propagation of acute lung injury have not been understood completely. A growing amount of evidence indicates that microRNAs (miRNAs) are involved in various human diseases. However, the expression profile and function of miRNAs in acute lung injury have not been investigated. Here, using real-time polymerase chain reaction analysis, we show that a collection of miRNAs is dynamically regulated in lipopolysaccharide (LPS)-induced mouse acute lung injury. Among them, miR-199a and miR-16 are the most significantly down-regulated miRNAs. To study the role of miR-199a and miR-16 in acute lung injury, an over-expression of miR-199a or miR-16 assay was performed in LPS-treated A549 cells, and then the expression of inflammatory factors was analyzed. Over-expression of miR-199a could not alter the expression level of interleukin (IL)-6 and tumor necrosis factor-alpha (TNFα), while up-regulation of miR-16 could significantly down-regulate IL-6 and TNFα expression level. Using bioinformatic analysis, we show that a 3' untranslational region (UTR) of IL-6 and TNFα contains the binding sites of miR-16. Accordingly, over-expression of miR-16 could significantly suppress the luciferase activity of reporter fusion with the binding sites of TNFα in its 3'UTR region, suggesting that miR-16 played its role in LPS-induced lung inflammation by a direct manner. In this study, we show for the first time that miRNAs are dynamically regulated and play an important function in LPS-induced lung 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) | 0.000 | 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 | 0.001 |
| 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".