Anti-LG3 Antibodies Aggravate Renal Ischemia–Reperfusion Injury and Long-Term Renal Allograft Dysfunction
Bibliographic record
Abstract
Pretransplant autoantibodies to LG3 and angiotensin II type 1 receptors (AT1R) are associated with acute rejection in kidney transplant recipients, whereas antivimentin autoantibodies participate in heart transplant rejection. Ischemia-reperfusion injury (IRI) can modify self-antigenic targets. We hypothesized that ischemia-reperfusion creates permissive conditions for autoantibodies to interact with their antigenic targets and leads to enhanced renal damage and dysfunction. In 172 kidney transplant recipients, we found that pretransplant anti-LG3 antibodies were associated with an increased risk of delayed graft function (DGF). Pretransplant anti-LG3 antibodies are inversely associated with graft function at 1 year after transplantation in patients who experienced DGF, independent of rejection. Pretransplant anti-AT1R and antivimentin were not associated with DGF or its functional outcome. In a model of renal IRI in mice, passive transfer of anti-LG3 IgG led to enhanced dysfunction and microvascular injury compared with passive transfer with control IgG. Passive transfer of anti-LG3 antibodies also favored intrarenal microvascular complement activation, microvascular rarefaction and fibrosis after IRI. Our results suggest that anti-LG3 antibodies are novel aggravating factors for renal IRI. These results provide novel insights into the pathways that modulate the severity of renal injury at the time of transplantation and their impact on long-term outcomes.
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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".