Flow dynamics within a bioreactor for tissue engineering by residence time distribution analysis combined with fluorescence and magnetic resonance imaging to investigate forced permeability and apparent diffusion coefficient in a perfusion cell culture chamber
Bibliographic record
Abstract
This study reveals that residence time distribution (RTD) analysis with pH monitoring after acid bolus injection can be used to globally study the flow dynamics of a perfusion bioreactor, while fluorescence microscopy and magnetic resonance imaging (MRI) were used to locally investigate mass transport within a hydrogel scaffold seeded or not with cells. The bioreactor used in this study is a close-loop tubular reactor. A dispersion model in one dimension has been used to describe the non-ideal behavior of the reactor. From open-loop experiments (single-cycle analysis), the presence of stagnant zones and back mixing were observed. The impact of the flow rate, the compliance chamber volume and mixing were investigated. Intermediate flows (30, 45, 60, and 90 mL min(-1)) had no effect over RTD function expressed in reduced time (θ). Lower flow rates (5 and 15 mL min(-1)) were associated to smaller extent of dispersion. The compliance chamber volume greatly affected the dynamics of the RTD function, while the effects of mixing and flow were small to non-significant. An empirical equation has been proposed to localize minima of the RTD function and to predict Per . Finally, cells seeded in a gelatin gel at a density of 800,000 cells mL(-1) had no effect over the permeability and the apparent diffusion coefficient, as revealed by fluorescent microscopy and MRI experiments.
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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 | 0.001 |
| 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".