The Role of the Dynamic Lung Extracellular Matrix Environment on Fibroblast Morphology and Inflammation
Bibliographic record
Abstract
The extracellular matrix (ECM) supports lung tissue architecture and physiology by providing mechanical stability and elastic recoil. Over the last several decades, it has become increasingly clear that the stiffness of the ECM governs many cellular processes, including cell-phenotype and functions during development, healing, and disease. Of all the lung ECM proteins, collagen-I is the most abundant and provides tensile strength. In many fibrotic lung diseases, the expression of collagen is increased which affects the stiffness of the surrounding environment. The goal of this study was to assess the effect on fibroblast morphology, cell death, and inflammation when exposed to 2D and 3D low (0.4 mg/mL) versus high (2.0 mg/mL) collagen-I-matrix environments that model the mechanics of the breathing lung. This study demonstrates that human fetal lung fibroblasts (HFL1), grown in a 3D collagen type-I environment compared to a 2D one, do not form cells with a myofibroblast morphology, express less F-actin stress fibers, exhibit less cell death, and significantly produce less pro-inflammatory IL-6 and IL-8 cytokines. Exposure to mechanical strain to mimic breathing (0.2 Hz) led to the loss of HFL1 fibroblast dendritic extensions as well as F-actin stress fibers within the cell cytoskeleton, but did not influence cytokine production or cell death. This dynamic assay gives researchers the ability to consider the assessment of the mechanodynamic nature of the lung ECM environment in disease-relevant models and the potential of mechano-pharmacology to identify therapeutic targets for treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".