Antiperlecan Antibodies Are Novel Accelerators of Immune-Mediated Vascular Injury
Bibliographic record
Abstract
Acute vascular rejection (AVR) is characterized by immune-mediated vascular injury and heightened endothelial cell (EC) apoptosis. We reported previously that apoptotic ECs release a bioactive C-terminal fragment of perlecan referred to as LG3. Here, we tested the possibility that LG3 behaves as a neoantigen, fuelling the production of anti-LG3 antibodies of potential importance in regulating allograft vascular injury. We performed a case-control study in which we compared anti-LG3 IgG titers in kidney transplant recipients with AVR (n=15) versus those with acute tubulo-interstitial rejection (ATIR) (n=15) or stable graft function (n=30). Patients who experienced AVR had elevated anti-LG3 titers pre and posttransplantation compared to subjects with ATIR or stable graft function (p<0.05 for both mediators). Elevated pretransplant anti-LG3 titers (OR: 4.62, 95% CI: 1.08-19.72) and pretransplant donor-specific antibodies (DSA) (OR 4.79, 95% CI: 1.03-22.19) were both independently associated with AVR. To address the functional role of anti-LG3 antibodies in AVR, we turned to passive transfer of anti-LG3 antibodies in an animal model of vascular rejection based on orthotopic aortic transplantation between fully MHC-mismatched mice. Neointima formation, C4d deposition and allograft inflammation were significantly increased in recipients of an ischemic aortic allograft passively transferred with anti-LG3 antibodies. Collectively, these data identify anti-LG3 antibodies as novel accelerators of immune-mediated vascular injury and obliterative remodeling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".