3D in vitro hydrogel models to study the human lung extracellular matrix and fibroblast function
Bibliographic record
Abstract
The pulmonary extracellular matrix (ECM) is a macromolecular structure that provides mechanical support, stability and elastic recoil for different pulmonary cells including the lung fibroblasts. The ECM plays an important role in lung development, remodeling, repair, and the maintenance of tissue homeostasis. Biomechanical and biochemical signals produced by the ECM regulate the phenotype and function of various cells including fibroblasts in the lungs. Fibroblasts are important lung structural cells responsible for the production and repair of different ECM proteins (e.g., collagen and fibronectin). During lung injury and in chronic lung diseases such as asthma, idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD), an abnormal feedback between fibroblasts and the altered ECM disrupts tissue homeostasis and leads to a vicious cycle of fibrotic changes resulting in tissue remodeling. In line with this, using 3D hydrogel culture models with embedded lung fibroblasts have enabled the assessment of the various mechanisms involved in driving defective (fibrotic) fibroblast function in the lung's 3D ECM environment. In this review, we provide a summary of various studies that used these 3D hydrogel models to assess the regulation of the ECM on lung fibroblast phenotype and function in altered lung ECM homeostasis in health and in chronic respiratory disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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 | 0.002 |
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".