Recombinant apoptosis inhibitor of macrophage protein reduces delayed graft function in a murine model of kidney transplantation
Bibliographic record
Abstract
Reperfusion injury following cold and warm ischemia (IRI) is unavoidable during kidney transplantation and contributes to delayed graft function (DGF) and premature graft loss. Death of tubular epithelial cells (TECs) by necrosis during IRI releases pro-inflammatory mediators (e.g. HMGB1), propagating further inflammation (necroinflammation) and tissue damage. Kidney Injury Molecule-1 (KIM-1) is a phagocytic receptor upregulated on proximal TECs during acute kidney injury. We have previously shown that renal KIM-1 protects the graft against transplant associated IRI by enabling TECs to clear apoptotic and necrotic cells, and that recognition of necrotic cells by KIM-1 is augmented in the presence of the opsonin, apoptosis inhibitor of macrophages (AIM). Here, we tested whether recombinant AIM (rAIM) could be used to mitigate transplant associated IRI. We administered rAIM or vehicle control to nephrectomised B6 mice transplanted with a single B6 donor kidney. Compared to grafts in vehicle-treated recipients, grafts from rAIM-treated mice exhibited significantly less renal dysfunction, tubular cell death, tissue damage, tubular obstruction, as well as local and systemic inflammation. Both mouse and human rAIM enhanced the clearance of necrotic cells by murine and human TECs, respectively in vitro. These data support testing of rAIM as a potential therapeutic agent to reduce DGF following kidney transplantation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".