Simple Modular Bioreactors for Tissue Engineering: A System for Characterization of Oxygen Gradients, Human Mesenchymal Stem Cell Differentiation, and Prevascularization
Bibliographic record
Abstract
Large-scale tissue engineering is limited by nutrient perfusion and mass transport limitations, especially oxygen diffusion, which restrict construct development to smaller than clinically relevant dimensions and limit the ability for in vivo integration. The goal of this work was to develop a modular approach to tissue engineering, where scaffold and tissue size, transport issues, and surgical implantation in vivo are considered from the outset. Human mesenchymal stem cells (hMSCs) were used as the model cell type, as their differentiation has been studied for several different cell lineages and often with conflicting results. Changes in the expression profiles of hMSCs differentiated under varied oxygen tensions are presented, demonstrating tissue-specific oxygen requirements for both adipogenic (20% O₂) and chondrogenic (5% O₂) differentiation. Oxygen and nutrient transport were enhanced by developing a bioreactor system for perfusing hMSC-seeded collagen gels using porous silk tubes, resulting in enhanced oxygen transport and cell viability within the gels. These systems are simple to use and scaled for versatility, to allow for the systematic study of relationships between cell content, oxygen, and cell function. The data may be combined with oxygen transport modeling to derive minimally sized modular units for construction of clinically relevant tissue-engineered constructs, a generic strategy that may be employed for vascularized target tissues.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".