Influence of loading and matrix stiffness on airway smooth muscle contractile function and phenotype within a 3D microtissue culture model
Bibliographic record
Abstract
Airway remodeling characteristic of asthma involves structural changes altering the elasticity of the airway smooth muscle (ASM) microenvironment potentially leading to ASM dysfunction. This effect of matrix stiffness was investigated using a physiologically relevant 3D culture model. Characterisation of microtissue responses with regards to contractile function and gene expression were studied varying the ECM stiffness and through stimulation with epithelial cell (AEC) conditioned media. ASM microtissues were fabricated under four different loading conditions and the matrix stiffness was increased by crosslinking through non-enzymatic glycation and increasing the collagen density. Function was assessed through the use of pharmacological agents and by imaging microcantilever deflection, used to calculate force generation. Crosslinking microtissues enhanced contractile function in response to agonists; however, this effect disappeared in microtissues tethered to stiff microcantilevers suggesting a limit of contractility within this model. Remarkably, there was a differential response in ASM function where increasing the collagen density (stiffness) significantly attenuated function. Additionally, contractility was significantly enhanced when chronically stimulated with AEC media. ASM tissue in 3D culture is responsive to the microenvironment stiffness and increases contractility in the presence of a stiffer ECM. This could occur with thickening of the airway wall in asthma. Decreased contractility with increased collagen density is in agreement with previous studies where it was shown that type I collagen is pro-proliferative and attenuates the contractile phenotype. We show the models ability to quantitatively demonstrate the impact of biomechanical cues on ASM function providing provides new ways to elucidate the mechanisms of cellular remodeling.
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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 | 0.001 | 0.001 |
| 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".