A Micro RNA Processing Defect in Rapidly Progressing Idiopathic Pulmonary Fibrosis
Bibliographic record
Abstract
BACKGROUND: Idiopathic pulmonary fibrosis exhibits differential progression from the time of diagnosis but the molecular basis for varying progression rates is poorly understood. The aim of the present study was to ascertain whether differential miRNA expression might provide one explanation for rapidly versus slowly progressing forms of IPF. METHODOLOGY AND PRINCIPAL FINDINGS: miRNA and mRNA were isolated from surgical lung biopsies from IPF patients with a clinically documented rapid or slow course of disease over the first year after diagnosis. A quantitative PCR miRNA array containing 88 of the most abundant miRNA in the human genome was used to profile lung biopsies from 9 patients with rapidly progressing IPF, 6 patients with slowly progressing IPF, and 10 normal lung biopsies. Using this approach, 11 miRNA were significantly increased and 36 were significantly decreased in rapid biopsies compared with normal biopsies. Slowly progressive biopsies exhibited 4 significantly increased miRNA and 36 significantly decreased miRNA compared with normal lung. Among the miRNA present in IPF with validated mRNA targets were those with regulatory effects on epithelial-mesenchymal transition (EMT). Five miRNA (miR-302c, miR-423-5p, miR-210, miR-376c, and miR-185) were significantly increased in rapid compared with slow IPF lung biopsies. Additional analyses of rapid biopsies and fibroblasts grown from the same biopsies revealed that the expression of AGO1 and AGO2 (essential components of the miRNA processing RISC complex) were lower compared with either slow or normal lung biopsies and fibroblasts. CONCLUSION: These findings suggest that the development and/or clinical progression of IPF might be the consequence of aberrant miRNA processing.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".