Heterotopic Renal Autotransplantation in a Porcine Model: A Step-by-Step Protocol
Bibliographic record
Abstract
Kidney transplantation is the treatment of choice for patients suffering from end-stage renal disease. It offers better life expectancy and higher quality of life when compared to dialysis. Although the last few decades have seen major improvements in patient outcomes following kidney transplantation, the increasing shortage of available organs represents a severe problem worldwide. To expand the donor pool, marginal kidney grafts recovered from extended criteria donors (ECD) or donated after circulatory death (DCD) are now accepted for transplantation. To further improve the postoperative outcome of these marginal grafts, research must focus on new therapeutic approaches such as alternative preservation techniques, immunomodulation, gene transfer, and stem cell administration. Experimental studies in animal models are the final step before newly developed techniques can be translated into clinical practice. Porcine kidney transplantation is an excellent model of human transplantation and allows investigation of novel approaches. The major advantage of the porcine model is its anatomical and physiological similarity to the human body, which facilitates the rapid translation of new findings to clinical trials. This article offers a surgical step-by-step protocol for an autotransplantation model and highlights key factors to ensure experimental success. Adequate pre- and postoperative housing, attentive anesthesia, and consistent surgical techniques result in favorable postoperative outcomes. Resection of the contralateral native kidney provides the opportunity to assess post-transplant graft function. The placement of venous and urinary catheters and the use of metabolic cages allow further detailed evaluation. For long-term follow-up studies and investigation of alternative graft preservation techniques, autotransplantation models are superior to allotransplantation models, as they avoid the confounding bias posed by rejection and immunosuppressive medication.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".