miR-484: A Possible Indicator of Drug-Induced Pulmonary Fibrosis
Bibliographic record
Abstract
BACKGROUND: Drug-induced lung injury leads to serious lung diseases, such as pulmonary fibrosis. We demonstrated in an alveolar epithelial cell line A549/ABCA3 that certain microRNAs were associated with bleomycin induced epithelial-mesenchymal transition (EMT) which is closely related to pulmonary fibrosis. In this study, we focused on the role of miR-484 in drug-induced EMT using A549/ABCA3 cells and a mouse lung injury model. METHODS: The expression of EMT-related genes and miR-484 was detected by real-time polymerase chain reaction. miR-484-targeted proteins were analyzed by Western blot. Pulmonary fibrosis mouse model was prepared by the intratracheal administration of BLM. As miR-484 is known to target SMAD2 and zinc finger E-box binding homeobox 1 (ZEB1), which are the well-known EMT-related transcription factors, we assessed the effects of a miR-484 inhibitor or mimic on the mRNA/protein expression of both the factors. RESULTS: We found that bleomycin significantly suppressed the intracellular expression and extracellular release of miR-484 in A549/ABCA3 cells. Moreover, the miR-484 mimic and inhibitor showed no drastic effects on the expression of the EMT-related transcription factors. In addition, the miR-484 mimic had no effect on the bleomycin-induced altered mRNA expression of the α-smooth muscle actin, a representative EMT marker. This suggested that miR-484 did not directly contribute to bleomycin-induced EMT in A549/ABCA3 cells. In contrast, the significant decrease in miR-484 expression in the lung tissue or plasma of bleomycin-administered mice suggested that miR-484 expression was closely correlated with bleomycin-induced lung injury. CONCLUSIONS: These findings indicate that miR-484 could be a novel diagnostic indicator for drug-induced pulmonary fibrosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".