Identification of risk epitope mismatches associated with de novo donor-specific HLA antibody development in cardiothoracic transplantation
Bibliographic record
Abstract
The development of de novo donor-specific HLA antibodies (dnDSA) after transplantation is associated with graft failure, mortality, and cost. There is no effective therapeutic intervention to prevent dnDSA or ameliorate associated injury. The aims of this study were to identify specific HLA factors associated with dnDSA development and to propose primary prevention strategies that could reduce the incidence of dnDSA without prohibitively limiting access to transplant. The investigation cohort included heart transplant recipients from 2008 to 2015 (n = 265). HLA typing was performed and HLA antibody testing was undertaken before and after transplantation. HLAMatchmaker analysis was performed for persistent dnDSA to identify potentially more immunogenic eplet differences. Validation was performed in recipients of lung transplants from 2008 to 2013 (n = 433). The majority of recipients with dnDSA had antibodies to identical eplet positions on DQ2 and DQ7. A high-risk epitope mismatch (found in DQA1*05 + DQB1*02/DQB1*03:01(7)) was associated with a 4.2- and 4.9-fold increased risk of dnDSA in heart and lung recipients respectively. HLA electrostatic potential modeling provided a plausible explanation for this observed immunogenicity. A theoretical allocation algorithm avoiding high-risk epitope mismatches was generated and predicted to reduce dnDSA by up to 72% without additional testing, eplet analysis, or cost.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 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".