Developing defined substrates for stem cell culture and differentiation
Bibliographic record
Abstract
Over the past few decades, a variety of different reagents for stem cell maintenance and differentiation have been commercialized. These reagents share a common goal in facilitating the manufacture of products suitable for cell therapy while reducing the amount of non-defined components. Lessons from developmental biology have identified signalling molecules that can guide the differentiation process in vitro , but less attention has been paid to the extracellular matrix used. With the introduction of more biologically relevant and defined matrices, that better mimic specific cell niches, researchers now have powerful resources to fine-tune their in vitro differentiation systems, which may allow the manufacture of therapeutically relevant cell types. In this review article, we revisit the basics of the extracellular matrix, and explore the important role of the cell–matrix interaction. We focus on laminin proteins because they help to maintain pluripotency and drive cell fate specification. This article is part of the theme issue ‘Designer human tissue: coming to a lab near you’.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".