The effect of the mechanodynamic lung environment on fibroblast phenotype via the Flexcell
Bibliographic record
Abstract
strain cycles at a 25% amplitude during heavy exercises, sighs, and deep inspirations. These mechanical indices have been reported to become aberrant in lung diseases such as acute respiratory distress syndrome (ARDS), pulmonary hypertension, bronchopulmonary dysplasia (BPD), idiopathic pulmonary fibrosis (IPF), and asthma. Through recent innovations, various in vitro systems/bioreactors used to mimic the lung's mechanical strain have been developed. Among these, the Flexcell tension system which is composed of bioreactors that utilize a variety of programs in vitro to apply static and cyclic strain on different cell-types established as 2D monolayer cultures or cell-embedded 3D hydrogel models, has enabled the assessment of the response of different cells such as fibroblasts to the lung's mechanical strain in health and disease. Fibroblasts are the main effector cells responsible for the production of extracellular matrix (ECM) proteins to repair and maintain tissue homeostasis and are implicated in the excessive deposition of matrix proteins that leads to lung fibrosis. In this review, we summarise, studies that have used the Flexcell tension bioreactor to assess effects of the mechanical lung on the structure, function, and phenotype of lung fibroblasts in homeostatic conditions and abnormal environments associated with lung injury and disease. We show that these studies have revealed that different strain conditions regulate fibroblast proliferation, ECM protein production, and inflammation in normal repair and the diseased lung.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".