Anti-asthmatic miR-224-5p inhibits the FHL1/MAPK pathway to repress airway smooth muscle cell proliferation in a murine model of asthma-like airway inflammation
Bibliographic record
Abstract
BACKGROUND: The proliferation of airway smooth muscle cells (ASMCs) contributes to the contractility and inflammation in the pathophysiology of asthma. This intrigued us to clarify the effect of microRNA (miR)-224-5p on biological characteristics of ASMCs in mice with asthma-like airway inflammation and responses through the FHL1-dependent MAPK pathway. METHODS: An ovalbumin (OVA)-induced asthma mouse model was established, where ASMCs were isolated. The expression of FHL1 was determined in asthmatic mice. Artificial modulation of FHL1 expression was performed to explore its effect on airway inflammation of asthmatic mice and ASMC proliferation and apoptosis. Afterwards, we analyzed the interaction among miR-224-5p, FHL1 and the MAPK pathway, and explored their combined impacts on airway inflammation of asthmatic mice and ASMC proliferation and apoptosis. RESULTS: FHL1 was highly expressed and miR-224-5p was poorly expressed in asthmatic mice. FHL1 was verified to be a target of miR-224-5p. Loss of FHL1 function reduced airway inflammation in asthmatic mice and proliferation of ASMCs while inducing their apoptosis. Besides, miR-224-5p inhibited the MAPK pathway by binding to FHL1. Overexpression of miR-224-5p relieved airway inflammation, inhibited ASMC proliferation, and increased apoptosis, which could be reversed by overexpression of FHL1. CONCLUSION: Altogether, miR-224-5p inhibited airway inflammation in asthmatic mice and ASMC proliferation through blocking the MAPK pathway by down-regulating FHL1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".