Matchmaker, matchmaker make me a match: Opportunities and challenges in optimizing compatibility of HLA eplets in transplantation
Bibliographic record
Abstract
The development of donor-specific antibodies (DSAs) is a major complication in transplantation, which is associated with inferior graft survival, impaired quality of life, and increased healthcare costs. DSA develop upon recognition of nonself HLA by the recipient's immune system. HLA molecules contain epitopes, which are the surface regions of HLA molecules recognized by antibodies. HLAMatchmaker is an algorithm for assessing donor:recipient HLA compatibility at the level of structurally defined HLA targets called eplets. The consideration of eplets, rather than the whole HLA molecule, could offer some advantages when classifying the immune risk associated with particular donor:recipient pairs. Assessing compatibility at the level of HLA eplets could decrease misclassification of post-transplant immune risk by improving specificity, when antibodies are confirmed to be directed against donor eplets missing from the recipient's repertoire of eplets. Consideration of eplets may also increase the sensitivity of immune risk assessment, when identifying mismatched eplets that could give rise to new, not previously detected, donor-specific antibodies post-transplant. Eplet matching can serve as a rational strategy for immune risk mitigation. Herein, we review the evolution of HLA (in) compatibility assessment for organ allocation. We outline challenges in the implementation of eplet-based donor:recipient matching, including unavailability of allele-level donor genotypes for 11 HLA loci at the time of organ allocation and difficulty in assessing the hierarchy of immune risk associated with particular HLA eplet mismatches. Opportunities to address some of the current shortcomings of donor genotyping and HLAMatchmaker are also discussed. While there is a demonstrated benefit in the application of HLAMatchmaker for donor: recipient HLA (in)compatibility assessment, evolving long-read genotyping methods, compilation of large data sets with allele-level genotypes, and standardization of methods to verify eplets as determinants of immune-mediated injuries are required before HLA eplet matching is implemented in organ allocation to improve upon transplant outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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 teacher head, 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".