Computational fluid dynamics for enhanced tracheal bioreactor design and long-segment graft recellularization
Bibliographic record
Abstract
Successful re-epithelialization of de-epithelialized tracheal scaffolds remains a challenge for tracheal graft success. Currently, the lack of understanding of the bioreactor hydrodynamic environment, and its relation to cell seeding outcomes, serve as major obstacles to obtaining viable tracheal grafts. In this work, we used computational fluid dynamics to (a) re-design the fluid delivery system of a trachea bioreactor to promote a spatially uniform hydrodynamic environment, and (b) improve the perfusion cell seeding protocol to promote homogeneous cell deposition. Lagrangian particle-tracking simulations showed that low rates of rotation provide more uniform circumferential and longitudinal patterns of cell deposition, while higher rates of rotation only improve circumferential uniformity but bias cell deposition proximally. Validation experiments with human bronchial epithelial cells confirm that the model accurately predicts cell deposition in low shear stress environments. We used the acquired knowledge from our particle tracking model, as a guide for long-term tracheal repopulation studies. Cell repopulation using conditions resulting in low wall shear stress enabled enhanced re-epithelialization of long segment tracheal grafts. While our work focuses on tracheal regeneration, lessons learned in this study, can be applied to culturing of any tissue engineered tubular scaffold.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".