Towards the repair of marginal liver grafts: Mesenchymal stromal cell therapy.
Bibliographic record
Abstract
Abstract Despite the recent inclusion of livers donated after cardiac death (DCD), more than 25% of people listed for a liver transplant in Canada die before obtaining a suitable graft. 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 for transplantation. Mesenchymal Stromal cells (MSC) show promise in protecting liver tissue against HIRI: MSC stimulated with inflammatory agents have enhanced anti-inflammatory activity. Kupffer Cells (KCs) are central to HIRI, are activated after reperfusion, and release various inflammatory mediators that promote hepatocyte death. We hypothesize that LPS-primed MSC-treatment can mitigate the tissue damage inflicted by HIRI by modulating KC activation. To test this, we injected LPS-primed MSCs(LPS) into rats undergoing warm ischemia to 70% of the liver. Rats that received MSCs(LPS) had significantly lower levels of AST (2288 ± 2121 IU/L) compared to sham controls (6614 ± 3495 IU/L). Histological analysis showed decreased TUNEL and Caspase-3 positive staining in the MSC(LPS) group: the necrotic area in the MSC- and sham-treated groups affected >50% of the liver parenchyma whereas, only ~20% was necrotic in the MSC(LPS) group. In this latter group, the number of infiltrating inflammatory CD68+ macrophages in the necrotic tissue and CD163+ macrophages in the viable tissue was significantly decreased. Systemic treatment of rats with activated MSCs resulted in the decreased migration of macrophages into inflamed tissue and limited parenchymal tissue damage. Primed MSCs are a viable treatment to protect marginal DCD donor liver grafts from the damaging effects of HIRI.
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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) | 0.001 | 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".