Influence of Biomechanical Stimulus on 3D Bioprinted Respiratory Tissue Scaffolds
Bibliographic record
Abstract
Abstract In respiratory tissue engineering, the development of respiratory tissue models or constructs is been challenging due to the complex structure of native respiratory tissue and the unique biomechanical conditions induced by breathing. While studies have shown that the inclusion of biomechanical stimulus that mimics physiological conditions greatly benefits the development of engineered tissues, to our knowledge no studies investigating the influence of biomechanical stimulus on the development of respiratory tissue models engineered by three-dimensional (3D) bioprinting have been reported. This paper presents a study on the inclusion of biomechanical stimulus during the culture of 3D bioprinted constructs for the development of respiratory tissue models with further investigation into the influence of biomechanical stimulus on the tissue constructs in terms of mechanical and biological properties. Constructs were bioprinted using an alginate/collagen hydrogel containing human primary pulmonary fibroblasts with further seeding of human primary bronchial epithelial cells. Biomechanical stimulus was then applied via a novel ventilated incubator developed for our study. Constructs were subject to four different pressure change and airflow conditions: standard incubation, shallow breathing, normal breathing and heavy breathing, over a two-week time period. At time points of 1, 3, 5, 7, 10, and 14 days, constructs were examined and characterized in terms of tensile mechanical properties, cell proliferation and cell morphology. The results illustrated that conditions mimicking normal and heavy breathing led to greater and more continuous cell proliferation, and greater production of vimentin, a structural protein produced by fibroblasts. Inclusion of biomechanical stimulus during culture of 3D bioprinted respiratory constructs promotes cellular growth and proliferation, indicating a more physiologically relevant respiratory tissue model.
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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".