Defining the structural basis for human leukocyte antigen reactivity in clinical transplantation
Bibliographic record
Abstract
The current state-of-the-art technology employed to assess anti-human leukocyte antigen antibodies (Anti-HLA Ab) for donor-recipient matching and patient risk stratification in renal transplantation is the single antigen bead (SAB) assay. However, there are limitations to the SAB assay as it is not quantitative and due to variations in techniques and reagents, there is no standardization across laboratories. In this study, a structurally-defined human monoclonal alloantibody was employed to provide a mechanistic explanation for how fundamental alloantibody biology influences the readout from the SAB assay. Performance of the clinical SAB assay was evaluated by altering Anti-HLA Ab concentration, subclass, and detection reagents. Tests were conducted in parallel by two internationally accredited laboratories using standardized protocols and reagents. We show that alloantibody concentration, subclass, laboratory-specific detection devices, subclass-specific detection reagents all contribute to a significant degree of variation in the readout. We report a significant prozone effect affecting HLA alleles that are bound strongly by the test alloantibody as opposed to those bound weakly and this phenomenon is independent of complement. These data highlight the importance for establishing international standards for SAB assay calibration and have significant implications for our understanding of discordance in previous studies that have analyzed its clinical relevance.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".