Influence of Breath-Mimicking Ventilated Incubation on Three-Dimensional Bioprinted Respiratory Tissue Scaffolds
Bibliographic record
Abstract
Development of respiratory tissue constructs is 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 mimicking 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 produced through three-dimensional (3D) bioprinting have been reported. This paper presents a study on the utilization of a novel breath-mimicking ventilated incubator to impart biomechanical stimulus during the culture of 3D respiratory bioprinted constructs. 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 capable of mimicking the pressure and airflow conditions of multiple breathing conditions: standard incubation, shallow breathing, normal breathing, and heavy breathing, over a two-week time period. At time points between 1 and 14 days, constructs were characterized in terms of mechanical properties, cell proliferation, and morphology. The results illustrated that incubation conditions mimicking normal and heavy breathing led to greater and more continuous cell proliferation and further indicated 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) | 0.000 | 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".