Functional characterization of 3D contractile smooth muscle tissues generated using a unique microfluidic 3D bioprinting technology
Bibliographic record
Abstract
Conditions such as asthma and inflammatory bowel disease are characterized by aberrant smooth muscle contraction. It has proven difficult to develop human cell-based models that mimic acute muscle contraction in 2D in vitro cultures due to the nonphysiological chemical and mechanical properties of lab plastics that do not allow for muscle cell contraction. To enhance the relevance of in vitro models for human disease, we describe how functional 3D smooth muscle tissue that exhibits physiological and pharmacologically relevant acute contraction and relaxation responses can be reproducibly fabricated using a unique microfluidic 3D bioprinting technology. Primary human airway and intestinal smooth muscle cells were printed into rings of muscle tissue at high density and viability. Printed tissues contracted to physiological concentrations of histamine (0.01-100 μM) and relaxed to salbutamol, a pharmacological compound used to relieve asthmatic exacerbations. The addition of TGFβ to airway muscle rings induced an increase in unstimulated muscle shortening and a decreased response to salbutamol, a phenomenon which also occurs in chronic lung diseases. Results indicate that the 3D bioprinted smooth muscle is a physiologically relevant in vitro model that can be utilized to study disease pathways and the effects of novel therapeutics on acute contraction and chronic tissue stenosis.
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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) | 0.000 | 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".