The Role of miRNAs in Extracellular Matrix Repair and Chronic Fibrotic Lung Diseases
Bibliographic record
Abstract
The lung extracellular matrix (ECM) plays a key role in the normal architecture of the lung, from embryonic lung development to mechanical stability and elastic recoil of the breathing adult lung. The lung ECM can modulate the biophysical environment of cells through ECM stiffness, porosity, topography and insolubility. In a reciprocal interaction, lung ECM dynamics result from the synthesis, degradation and organization of ECM components by the surrounding structural and immune cells. Repeated lung injury and repair can trigger a vicious cycle of aberrant ECM protein deposition, accompanied by elevated ECM stiffness, which has a lasting effect on cell and tissue function. The processes governing the resolution of injury repair are regulated by several pathways; however, in chronic lung diseases such as asthma, chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary disease (IPF) these processes are compromised, resulting in impaired cell function and ECM remodeling. Current estimates show that more than 60% of the human coding transcripts are regulated by miRNAs. miRNAs are small non-coding RNAs that regulate gene expressions and modulate cellular functions. This review is focused on the current knowledge of miRNAs in regulating ECM synthesis, degradation and topography by cells and their dysregulation in asthma, COPD and IPF.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".