Prediction of Cell Growth Over a Circular Strand of a Scaffold Using the Lattice Boltzmann Method
Bibliographic record
Abstract
The role of a bioreactor during the in vitro tissue culture process is important as the cell growth can be significantly enhanced by providing adequate nutrient supply and favorable mechanical stimuli, i.e. shear stress to the cells. Within a certain range, flow-induced shear stress has a positive impact on the cell growth. In the past, bioreactors and scaffold structures were designed based on empirical evidence, e.g. the flow rate inside the bioreactor was selected based on a trial and error method. More recently, mathematical and computational modeling of such complex process has been able to provide insight into the culture process and predict the overall cell and tissue growth. Although a number of computational studies which provide such cell and tissue growth information can be found in the literature, a comprehensive simulation to predict the overall tissue growth based on the supply of multiple nutrients and shear stress level acting on the cells is not yet available. In this study a simultaneous fluid flow and mass transfer analysis has been performed using the lattice Boltzmann method. A cell growth equation which considers the transport of multiple nutrients as well as the shear stress induced on the cells to predict the cell growth rate has been implemented. The overall model integrates the momentum, convection-diffusion and cell growth equations in a coupled fashion to predict the cell growth on a single strand of a scaffold placed inside a perfusion bioreactor. It is found that the maximum shear stress on the strand surface occurs near the strand shoulder. The nutrient supply and cell growth rate at the front of the cylinder is about ten times higher than at the rear of the cylinder. The cell growth rate obtained in the present study compares well with the results of other models documented in the literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".