Bioengineered Kidney Regeneration and Transplantation: Progress, Challenges, and Translational Prospects
Bibliographic record
Abstract
A major limitation of the treatment for ESRD has been, and continues to be, the shortfall in available donor organs; this situation has fostered greater interest in the areas of regenerative medicine and bioengineered organ substitutes. Among all solid organs, the kidney represents one of the most complex targets for tissue engineering due to its highly specialized microarchitecture, dense vascularization, and integrated filtration and excretory functions. Recent advances in decellularization–recellularization technologies have demonstrated the feasibility of generating bioengineered kidneys capable of limited physiological function in preclinical models. This narrative review critically examines progress in kidney bioengineering, with particular emphasis on scaffold-based regeneration strategies, cellular repopulation approaches, bioreactor conditioning, and experimental transplantation outcomes. Animal studies have shown that acellular renal scaffolds prepared from native organs can maintain extracellular matrix cues that support cell adhesion, differentiation, and vascular reconstruction. Recellularization with endothelial and renal epithelial cells has allowed for partial restoration of filtration and urine production following orthotopic transplantation in rodent models. Although functional output remains substantially lower than that of native kidneys, even modest renal activity may have meaningful clinical implications for patients dependent on dialysis. This review synthesizes current experimental findings, discusses methodological limitations, and evaluates translational challenges, including immune compatibility, long-term graft viability, and scalability for human application. By integrating biological, engineering, and clinical perspectives, the paper highlights bioengineered kidneys as a promising yet evolving strategy that may one day complement or transform conventional renal replacement therapies.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".