Mass Transfer Limitations in Embryoid Bodies during Human Embryonic Stem Cell Differentiation
Bibliographic record
Abstract
Due to their ability to differentiate into cell types from all the three germ layers and their potential unlimited capacity for expansion, embryonic stem cells have tremendous potential to treat diseases and injuries. Spontaneous differentiation of human embryonic stem cells (hESCs) is influenced by the size of the differentiating embryoid bodies (EBs). To further understand the dynamics between nutrient mass transfer, EB size, and stem cell differentiation, a transient mass diffusion model of a single hESC EB was constructed. The results revealed that the oxygen concentration at the centers of large EBs (400-μm radius) was 50% lower when compared to that in smaller EBs (200-μm radius). In addition, the concentration profile of cytokines within an EB depended strongly on their depletion rate, with higher depletion rates resulting in cytokine concentrations that varied significantly throughout the EB. A comparison of the results of our model with published experimental data reveals a close correlation between the fraction of cells that differentiate to a given lineage and the fraction of cells exposed to different oxygen or cytokine concentrations. This, along with other data from the literature, suggests that diffusive mass transfer influences the differentiation of hESCs within EBs by controlling the spatial distribution of soluble factors. This has important implications for research involving the differentiation of embryonic stem cells in EBs, as well as for bioprocess design and the development of robust differentiation protocols where mass transfer could be altered to control the cell differentiation trajectory.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".