234.3: Establishing the Most Physiological Perfusion Milieu For Normothermic Ex Vivo Pancreas Perfusion in Porcine Models: How Far Have We Reached?
Bibliographic record
Abstract
Introduction: The basic tenet of normothermic machine perfusion is to simulate the physiological milieu for perfusing the organ as close to the homeostasis as possible. Despite its success in lung and kidney transplant models, similar results could not be replicated in pancreas models owing to vulnerability of the organ to oedema and reperfusion injury. We, hereby summarise our experience so far in achieving the most conducive environment for the pancreas perfusion by altering composition of perfusing and dialysing fluids used in the machine to establish an “ideal” model of normothermic ex-vivo pancreas perfusion (NEVPP). Methods: The perfusate being used in NEVPP of porcine grafts (Yorkshire male pigs, 40-45 kgs) so far at our laboratory has been a modified version of our already established solution used in kidney perfusion models comprising of Steen’s solution (containing Dextran), leucocyte depleted porcine blood, electrolyte composition and trypsin inhibitor. In addition, a dialysing solution comprising of electrolytes and standard hemodialysis concentrate and sodium pyruvate is added to the circuit. By altering osmolality and sodium concentration of the perfusate (addition of hydroxyethyl starch and NaCl) and dialysate (addition of varying concentrations of NaCl), we assessed the physiological and biochemical parameters of the graft over 3 hrs of perfusion and compared with 3 best experiments from the previously established model (Standard NEVPP). Results: The average weight gain was found to be lower (25-33%) in grafts perfused with addition NaCl in the dialysate as opposed to the standard NEVPP model (45-50%). The best hemodynamic milieu (maximum arterial flow 200 ml/min at minimum pressure of 16 mm Hg) could be achieved by addition of 1gm/L of NaCl to dialysate along with increasing the CO2 concentration in the circuit to 9% as opposed to the standard 5%. The graft oedema as well as duodenal congestion was also minimum in this subset of the model (Figure 1). Addition of NaCl to the dialysate also resulted in achieving a more stable peak in the amylase levels (marker of graft injury) in the perfusate over 3 hrs compared to the standard NEVPP model (Figure 2). Similar trends were observed in this subset with lipase (average peak (3 hrs): 2885 U/L), LDH (average peak (3hrs): 576 U/L) and CPK (average peak (3hrs): 1120 U/L) levels. The oxygen extraction ratio remained in the range of 25-40 in this subset and was unaffected by altering the CO2 concentration to 9%. The histopathological comparison of tissue injury was also analysed. Conclusion: Owing to the precarious hemodynamics of pancreas, establishing optimal conditions for NEVPP is a formidable challenge. However, meticulous cold dissection, minimum handling of the organ while on the machine along with a relatively hyperosmolar composition of the dialysing solution has shown promising results so far.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".