Effectiveness factor and diffusion limitations in collagen gel modules containing HepG2 cells
Bibliographic record
Abstract
A major obstacle in tissue engineering is overcoming hypoxia in thick, three-dimensional (3D) engineered tissues, which is caused by the diffusional limitations of oxygen and lack of internal vasculature to facilitate mass transfer. Modular tissue engineering is a bio-mimetic strategy that forms scalable, vascularized and uniform 3D constructs by assembling small (sub-mm), cell-containing modules. It was previously assumed that mass transfer resistance within the individual modules was negligible, due to their small size. In the present study, this assumption was tested using theoretical analysis of oxygen transport within the module (effectiveness factor) and experimental studies. Small (400 µm diameter post-contraction) and large (700 µm diameter post-contraction) HepG2-collagen modules were made for a range of seeding densities (2 × 10(6) -1 × 10(7) cells/ml collagen). Cell density, distribution and morphology within the modules showed that the small modules were capable of sustaining high cell densities (8.0 × 10(7) ± 4.4 × 10(7) cells/cm(3) ) with negligible mass transfer inhibition. Conversely, large modules developed a necrotic core and had significantly (p < 0.05) reduced cell densities (1.5 × 10(7) ± 9.2 × 10(6) cells/cm(3) ). It was also observed that the embedded cells responded quickly to the oxygen availability, by proliferating or dying, to reach a sustainable density of approximately 8000 cells/module. Furthermore, a simple effectiveness factor calculation was successful in estimating the maximum cell density per module. The results gathered in this study confirm the previous assumption that the small-diameter modules avoid the internal mass transfer limitations that are often observed in larger constructs.
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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".