New strategies in kidney regeneration and tissue engineering
Bibliographic record
Abstract
PURPOSE OF REVIEW: The severe shortage of suitable donor kidneys limits organ transplantation to a small fraction of patients suffering from end-stage renal failure. Engineering autologous kidney grafts on-demand would potentially alleviate this shortage, thereby reducing healthcare costs, improving quality of life, and increasing longevity for patients suffering from renal failure. RECENT FINDINGS: Over the past 2 years, several studies have demonstrated that structurally intact extracellular matrix (ECM) scaffolds can be derived from human or animal kidneys through decellularization, a process in which detergent or enzyme solutions are perfused through the renal vasculature to remove the native cells. The future clinical paradigm would be to repopulate these decellularized kidney matrices with patient-derived renal stem cells to regenerate a functional kidney graft. Recent research aiming toward this goal has focused on the optimization of decellularization protocols, design of bioreactor systems to seed cells into appropriate compartments of the renal ECM to nurture their growth to restore kidney function, and differentiation of pluripotent stem cells (PSCs) into renal progenitor lineages. SUMMARY: New research efforts utilizing bio-mimetic perfusion bioreactor systems to repopulate decellularized kidney scaffolds, coupled with the differentiation of PSCs into renal progenitor cell populations, indicate substantial progress toward the ultimate goal of building a functional kidney graft on-demand.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".