Total parenteral nutrition in ex vivo lung perfusion: Addressing metabolism improves both inflammation and oxygenation
Bibliographic record
Abstract
Ex vivo lung perfusion (EVLP) protocols generally limit metabolic supplementation to insulin and glucose. We sought to determine whether the addition of total parenteral nutrition (TPN) would improve lung function in EVLP. Ten porcine lungs were perfused using EVLP for 24 hours and supplemented with insulin and glucose. In the treatment group (n = 5), the perfusate was also supplemented with a continuous infusion of TPN containing lipids, amino acids, essential vitamins, and cofactors. Physiologic parameters and perfusate electrolytes were continuously evaluated. Perfusate lactate, lipid and branch chain amino acid (BCAA) concentrations were also analyzed to elucidate how substrates were being utilized over time. Lungs in the TPN group exhibited significantly better oxygenation. Perfusate sodium was more stable in the TPN group. In the control group, free fatty acids (FFA) were quickly depleted, reaching negligible levels early in the perfusion. Alternatively, BCAA in the control group rose continually over the perfusion demonstrating a shift toward proteolysis for energy substrate. In the TPN group, both FFA and BCAA concentrations remained stable at in vivo levels after initial stabilization. TNF-α concentrations were lower in the TPN group. The addition of TPN in EVLP allows for better electrolyte composition, decreased inflammation, and improved graft performance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".