β-Catenin in stromal progenitors controls medullary stromal development
Bibliographic record
Abstract
The renal stroma is a population of matrix-producing fibroblast cells that serves as a structural framework for the kidney parenchyma. The stroma also regulates branching morphogenesis and nephrogenesis. In the mature kidney, the stroma forms at least three distinct cell populations: the capsular, cortical, and medullary stroma. These distinct stromal populations have important functions in kidney development, maintenance of kidney function, and disease progression. However, the development, differentiation, and maintenance of the distinct stroma populations are not well defined. Using a mouse model with β-catenin deficiency in the stroma cell population, we demonstrate that β-catenin is not involved in the formation of the stromal progenitors nor in the formation of the cortical stroma population. In contrast, β-catenin does control the differentiation of stromal progenitors to form the medullary stroma. In the absence of stromal β-catenin, there is a marked reduction of medullary stromal markers. As kidney development continues, the maldifferentiated stromal cells locate deeper within the kidney tissue and are eliminated by the activation of an intrinsic apoptotic program. This leads to significant reductions in the medullary stroma population and the lack of medulla formation. Taken together, our results indicate that stromal β-catenin is essential for kidney development by regulating medulla formation through the differentiation of medullary stromal cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".