Towards the repair of marginal liver grafts: <i>Ex vivo</i> Mesenchymal stromal cell perfusion
Bibliographic record
Abstract
Abstract More than 25% of persons listed for a liver transplant in Canada die before obtaining a suitable liver graft. This occurs despite the recent inclusion of livers from deceased after cardiac death (DCD) organ donation. Over half of possible DCD livers undergo long periods of ischemia that sensitize the liver to reperfusion injury (HIRI) and render the liver grafts unsuitable (marginal) for transplantation. Mesenchymal Stromal cells (MSC) protect against HIRI by decreasing the production of inflammatory mediators secreted by liver resident macrophages. We hypothesized that MSC-treatment of marginal DCD grafts on an ex-vivo perfusion circuit (NEVLP) would repair HIRI damage. Kupffer Cells (KCs) are central to HIRI: they are activated after reperfusion and release various inflammatory cytokines, including TNFα, which promote hepatocyte death. We have developed a DCD liver graft model to evaluate the potential of MSC treatment. In this model, liver grafts undergo 45-minute warm ischemia followed by 2-hours of cold ischemia before being placed on a normothermic ex-vivo perfusion unit for 6 hours. We found that the infusion of MSCs into the ex-vivo perfusion circuit decreases the levels of TNFα in the perfusate. This data was confirmed in in vitro studies where the co-culture of LPS-activated KC with MSCs decreased the amount of TNFα in culture supernatants compared to KC alone cultures. The levels of TNFα in the supernatant were further decreased in cultures treated with LPS-primed MSCs. We propose that the combination of MSC treatment and NEVLP perfusion of DCD livers will repair HIRI-injured and can be rapidly translated to the clinic to expand the donor organ pool.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".