Effectiveness factor and diffusion limitations in collagen gel modules containing HepG2 cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 it