Development of a 3D Bioprinted Airway Smooth Muscle Model for Manipulating Structure and Measuring Contraction
Bibliographic record
Abstract
ABSTRACT The contractile function of airway smooth muscle (ASM) is inextricably linked to its mechanical properties and interaction with the surrounding mechanical environment. As tissue engineering approaches become more commonplace for studying lung biology, the inability to replicate realistic mechanical contexts for ASM will increasingly become a barrier to a fulsome understanding of lung health and disease. To address this knowledge gap, we describe the use of 3D bioprinting technology to generate a novel experimental model of ASM with a wide scope for modulating tissue mechanics. Using a stiffness modifiable alginate-collagen-fibrinogen bioink, we demonstrate that modulating the stiffness of free-floating ASM ‘bare rings’ is unfeasible; bioink conditions favorable for muscle formation produce structures that rapidly collapse. However, the creation of novel ‘sandwich’ and ‘spiderweb’ designs that encapsulate the ASM bundle within stiff acellular load bearing frames successfully created variable elastic loads opposing tissue collapse and contraction. Sandwich and spiderweb constructs demonstrated realistic actin filament organisation, generated significant baseline tone, and responded appropriately to acetylcholine, potassium chloride and cytochalasin D. Importantly, the two designs feasibly simulate different mechanical contexts within the lung. Specifically, the sandwich was relatively compliant and subject to plastic deformation under high contractile loads, whereas the stiffer spiderweb was more robust and only deformed minimally after repeated maximal contractions. Thus, our model represents a new paradigm for studying ASM contractile function in a realistic mechanical context. Moreover, it holds significant capacity to study the effects of ECM composition, multiple cell types and fibrosis on lung health and disease. GRANTS Natural Sciences and Engineering Research Council, Discovery Grant (Adrian West) Research Manitoba, New Investigator Operating Grant (Adrian West) Children’s Hospital Research Institute of Manitoba, Operating Grant (Adrian West) Canadian Foundation for Innovation, John R. Evans Leaders Fund (Adrian West) University of Manitoba, Manitoba Graduate Scholarship (Jeffery Osagie) Research Manitoba, Master’s Studentship Award (Jeffery Osagie) Research Manitoba, Master’s Studentship Award (Sanjana Syeda) Children’s Hospital Research Institute of Manitoba, Summer Studentship (Michelle Guimond) University of Manitoba, Jack Prior Memorial Undergraduate Student Research Award (Lumiere Parrenas) University of Manitoba, Undergraduate Research Award (Ahsen Haroon) University of Manitoba, UMSU Undergraduate Research Award (Philip Imasuen) The grant bodies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".