Dynamic mechanical stimulation of alveolar epithelial-fibroblast models using the Flexcell tension system to study of lung disease mechanisms
Bibliographic record
Abstract
Mechanical strain plays a significant role in lung pathophysiology. Current two-dimensional (2D) in vitro models fail to capture the lung's dynamic mechanical environment. We developed mechanically strained 2D and more complex three-dimensional (3D) alveolar epithelial-fibroblast co-cultures and organoids using the Flexcell cell stretching bioreactor. To do this we used readily available human A549 epithelial cells and MRC-5 lung fibroblasts to establish 2D and 3D alveolar co-cultures and collagen-I-gel-embedded organoid models in the Flexcell and then strained them at 18% amplitude, 0.4 Hz for 24 h to mimic a pathological environment. The impact of mechanical strain on cell proliferation, morphology, cytoskeletal and tight junctional protein expression, interleukin-6 and-8 (IL-6, IL-8) inflammatory cytokine release, and cell death were assessed. Mechanical strain significantly increased total cell counts in 3D co-cultures but not in 2D co-cultures, potentially signifying increased proliferation. Morphological analysis revealed a marked transition of fibroblasts into broadened shape cells under strain in the 3D co-cultures. This was in line with increased F-actin in 3D co-cultures after strain. The tight junctional protein zonula occludens-1 expression decreased after strain in all 2D and 3D models. Furthermore, exposure to strain increased the release of IL-6 and IL-8. Strain-induced cell death was also elevated across all models, particularly in 3D cultures. This study presents exploratory findings suggesting that in vitro mechanical multicellular alveolar models using the Flexcell system may replicate the dynamic environment of in vivo lung tissue. These multicellular models offer a valuable platform for investigating strain-induced cellular responses relevant to inflammatory and fibrotic mechanisms in lung diseases, particularly in exploring epithelial–mesenchymal interactions that may underlie disease progression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".