Acellular Mouse Kidney ECM can be Used as a Three-Dimensional Substrate to Test the Differentiation Potential of Embryonic Stem Cell Derived Renal Progenitors
Bibliographic record
Abstract
The development of strategies for tissue regeneration and bio-artificial organ development is based on our understanding of embryogenesis. Differentiation protocols attempt to recapitulate the signaling modalities of gastrulation and organogenesis, coupled with cell selection regimens to isolate the cells of choice. This strategy is impeded by the lack of optimal in vitro culture systems since traditional culture systems do not allow for the three-dimensional interaction between cells and the extracellular matrix. While artificial three-dimensional scaffolds are available, using the natural extracellular matrix scaffold is advantageous because it has a distinct architecture that is difficult to replicate. The adult extracellular matrix is predicted to mediate signaling related to tissue repair not embryogenesis but existing similarities between the two argues that the extracellular matrix will influence the differentiation of stem and progenitor cells. Previous studies using undifferentiated embryonic stem cells grown directly on acellular kidney ECM demonstrated that the acellular kidney supported cell growth but limited differentiation occurred. Using mouse kidney extracellular matrix and mouse embryonic stem cells we report that the extracellular matrix can support the development of kidney structures if the stem cells are first differentiated to kidney progenitor cells before being applied to the acellular organ.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".