Targeting Complement Pathways During Cold Ischemia and Reperfusion Prevents Delayed Graft Function
Bibliographic record
Abstract
The complement system plays a critical role in ischemia-reperfusion injury (IRI)-mediated delayed graft function (DGF). To better understand the roles of complement activation pathways in IRI in kidney transplantation, donor kidneys were treated ex vivo with terminal complement pathway (TP) inhibitor, anti-rat C5 mAb 18A10, or complement alternative pathway (AP) inhibitor TT30 for 28 h at 4°C pretransplantation in a syngeneic kidney transplantation rat model. All 18A10- and 67% of TT30-pretreated grafts, but only 16.7% of isotype control-pretreated grafts, survived beyond day 21 (p < 0.01). Inhibitor treatment in the final 45 min of 28-h cold ischemia (CI) similarly improved graft survival. Systemic posttransplant treatment with 18A10 resulted in 60% increased graft survival beyond day 21 (p < 0.01), while no TT30-treated rat survived > 6 days. Our results demonstrate that AP plays a prominent role during CI and that blocking either the AP or, more effectively the TP prevents ischemic injury and subsequent DGF. Multiple complement pathways may be activated and contribute to reperfusion injury; blocking the TP, but not the AP, posttransplant is effective in preventing reperfusion injury and increasing graft survival. These results demonstrate the feasibility of using complement inhibitors for prevention of DGF in humans.
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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) | 0.001 | 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".