Generation of Functioning Nephrons by Implanting Human Pluripotent Stem Cell-Derived Kidney Progenitors
Bibliographic record
Abstract
Human pluripotent stem cells (hPSCs) hold great promise for understanding kidney development and disease. We reproducibly differentiated three genetically distinct wild-type hPSC lines to kidney precursors that underwent rudimentary morphogenesis in vitro. They expressed nephron and collecting duct lineage marker genes, several of which are mutated in human kidney disease. Lentiviral-transduced hPSCs expressing reporter genes differentiated similarly to controls in vitro. Kidney progenitors were subcutaneously implanted into immunodeficient mice. By 12 weeks, they formed organ-like masses detectable by bioluminescence imaging. Implants included perfused glomeruli containing human capillaries, podocytes with regions of mature basement membrane, and mesangial cells. After intravenous injection of fluorescent low-molecular-weight dextran, signal was detected in tubules, demonstrating uptake from glomerular filtrate. Thus, we have developed methods to trace hPSC-derived kidney precursors that formed functioning nephrons in vivo. These advances beyond in vitro culture are critical steps toward using hPSCs to model and treat kidney diseases.
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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) | 0.000 | 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".