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Record W2280832603 · doi:10.3791/53765

Heterotopic Renal Autotransplantation in a Porcine Model: A Step-by-Step Protocol

2016· article· en· W2280832603 on OpenAlexaff
J. Moritz Kaths, Juan Echeverri, Nicolás Goldaracena, Kristine S. Louis, Paul S. F. Yip, Rohan John, István Mucsi, Anand Ghanekar, Darius Bägli, Markus Selzner, Lisa A. Robinson

Bibliographic record

VenueJournal of Visualized Experiments · 2016
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsToronto General HospitalHospital for Sick Children
Fundersnot available
KeywordsMedicineAutotransplantationTransplantationKidney transplantationSurgeryDialysisIntensive care medicineKidneyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.030
GPT teacher head0.435
Teacher spread0.405 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

Quick stats

Citations24
Published2016
Admission routes1
Has abstractyes

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