Generation of human embryonic stem cell‐derived mesoderm and cardiac cells using size‐specified aggregates in an oxygen‐controlled bioreactor
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
The ability to generate human pluripotent stem cell-derived cell types at sufficiently high numbers and in a reproducible manner is fundamental for clinical and biopharmaceutical applications. Current experimental methods for the differentiation of pluripotent cells such as human embryonic stem cells (hESC) rely on the generation of heterogeneous aggregates of cells, also called "embryoid bodies" (EBs), in small scale static culture. These protocols are typically (1) not scalable, (2) result in a wide range of EB sizes and (3) expose cells to fluctuations in physicochemical parameters. With the goal of establishing a robust bioprocess we first screened different scalable suspension systems for their ability to support the growth and differentiation of hESCs. Next homogeneity of initial cell aggregates was improved by employing a micro-printing strategy to generate large numbers of size-specified hESC aggregates. Finally, these technologies were integrated into a fully controlled bioreactor system and the impact of oxygen concentration was investigated. Our results demonstrate the beneficial effects of stirred bioreactor culture, aggregate size-control and hypoxia (4% oxygen tension) on both cell growth and cell differentiation towards cardiomyocytes. QRT-PCR data for markers such as Brachyury, LIM domain homeobox gene Isl-1, Troponin T and Myosin Light Chain 2v, as well as immunohistochemistry and functional analysis by response to chronotropic agents, documented the impact of these parameters on cardiac differentiation. This study provides an important foundation towards the robust generation of clinically relevant numbers of hESC derived cells.
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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 | 0.000 | 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 it