The mechanical memory of lung myofibroblasts
Bibliographic record
Abstract
Fibroblasts differentiate into the highly synthetic and contractile myofibroblast phenotype when exposed to substrates with an elastic modulus corresponding to pathologically stiff fibrotic tissue. Cellular responses to changes in substrate stiffness are typically analyzed after hours or days, which does not enable the monitoring of myofibroblast persistence, a hallmark of fibrosis. To determine long-lasting effects on the fibrotic behavior of lung fibroblasts, we followed a novel approach of explanting and repeatedly passaging fibroblasts on silicone substrates with stiffness representing various states of lung health. Fibrotic activity was determined by assaying for myofibroblast proliferation, cell contractility, expression of α-smooth muscle actin, extracellular matrix and active TGFβ1. As predicted, myofibroblast activity was low on healthy soft substrates and increased with increasing substrate stiffness. However, explanting and mechanically priming lung fibroblasts for 3 weeks on pathologically stiff substrates resulted in sustained myofibroblast activity even after the cells were returned to healthy soft cultures for 2 weeks. Such primed cells retained higher fibrotic activity than cells that had been exclusively cultured on soft substrates, and were not statistically different from cells continuously passaged on stiff surfaces. Inversely, priming lung fibroblasts for 3 weeks on soft substrates partially protected from myofibroblast activation after the shift to stiff substrates. Hence, mechano-sensed information relating to physical conditions of the local cellular environment could permanently induce fibrotic behavior of lung fibroblasts. This priming effect has important implications for the progression and persistence of aggressive fibrotic diseases such as idiopathic pulmonary fibrosis.
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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".