Class II HLA Epitope Matching—A Strategy to Minimize De Novo Donor-Specific Antibody Development and Improve Outcomes
Bibliographic record
Abstract
De novo donor-specific antibody (dnDSA) develops in 15-25% of renal transplant recipients within 5 years of transplantation and is associated with 40% lower graft survival at 10 years. HLA epitope matching is a novel strategy that may minimize dnDSA development. HLAMatchmaker software was used to characterize epitope mismatches at 395 potential HLA-DR/DQ/DP conformational epitopes for 286 donor-recipient pairs. Epitope specificities were assigned using single antigen HLA bead analysis and correlated with known monoclonal alloantibody epitope targets. Locus-specific epitope mismatches were more numerous in patients who developed HLA-DR dnDSA alone (21.4 vs. 13.2, p < 0.02) or HLA-DQ dnDSA alone (27.5 vs. 17.3, p < 0.001). An optimal threshold for epitope mismatches (10 for HLA-DR, 17 for HLA-DQ) was defined that was associated with minimal development of Class II dnDSA. Applying these thresholds, zero and 2.7% of patients developed dnDSA against HLA-DR and HLA-DQ, respectively, after a median of 6.9 years. Epitope specificity analysis revealed that 3 HLA-DR and 3 HLA-DQ epitopes were independent multivariate predictors of Class II dnDSA. HLA-DR and DQ epitope matching outperforms traditional low-resolution antigen-based matching and has the potential to minimize the risk of de novo Class II DSA development, thereby improving long-term graft outcome.
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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 | 0.001 |
| 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".