Stem Cells and the Kidney
Bibliographic record
Abstract
In the past few years, a number of studies have shown that stem cells can be found in virtually every organ of the adult organism. The kidney is not an exception, and resident stem cells have been identified both in the papilla and along the tubules. Of interest, kidney-bound stem cells have been identified also in the bone marrow. When injected, both resident and bone marrow-derived stem cells are able to reach the injured renal tissue and, once there, to differentiate into renal cells. The evidence that, in humans, some of the acute and most of the chronic renal damages lead to ESRD suggests that in normal conditions, the reservoir of stem cells (considering both resident and bone marrow-derived stem cells) is insufficient to allow a major renal regeneration. Probably the number of stem cells that are ready to intervene in an adult kidney are sufficient to compensate for the normal cell turnover but largely inadequate to counteract a major injury. This is confirmed further by the finding that, even by transplanting a syngenic bone marrow in rats with ablation of 5/6 of the renal function, it is not possible to increase the life expectancy of the animals. Altogether, this evidence suggests that, to clarify the potentiality of a stem cell therapy for renal diseases, experiments that aim to clarify the ideal concentration of stem cells to be injected and to identify the best way of administration are needed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".