Targeting MicroRNA Function in Respiratory Diseases: Mini-Review
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small non-coding RNA molecules that modulate expression of the majority of genes by inhibiting protein translation. Growing literature has identified functional roles for miRNAs across a broad range of biological processes. As such, miRNAs are recognized as potential disease biomarkers and novel targets for therapies. While several miRNA-targeted therapies are currently in clinical trials (e.g., for the treatment of hepatitis C virus infection and cancer), no therapies have targeted miRNAs in respiratory diseases in the clinic. In this mini-review, we review the current knowledge on miRNA expression and function in respiratory diseases, intervention strategies to target miRNA function, and considerations specific to respiratory diseases. Altered miRNA expression profiles have been reported in a number of respiratory diseases, including asthma, chronic obstructive pulmonary disease, cystic fibrosis, and idiopathic pulmonary fibrosis. These include alterations in isolated lung tissue, as well as sputum, bronchoalveolar lavage fluids and peripheral blood or serum. The observed alterations in easily accessible body fluids (e.g., serum) have been proposed as new biomarkers that may inform disease diagnosis and patient management. In a subset of studies, miRNA-targeted interventions also improved disease outcomes, indicating functional roles for altered miRNA expression in disease pathogenesis. In fact, direct administration of miRNA-targeting molecules to the lung has yielded promising results in a number of animal models. The ability to directly administer compounds to the lung holds considerable promise and may limit potential off-target effects and side effects caused by the systemic administration required to treat other diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".