The role of non-HLA antibodies in solid organ transplantation: a complex deliberation
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is tremendous interest in understanding when, if, and how non-HLA antibodies contribute to allograft injury. Numerous non-HLA target antigens have been identified and sensitization to these targets have been associated with delayed allograft function, rejection, and allograft failure. This review focuses on the clinical utility of HLA antibody testing, highlighting the strengths and limitations of current clinical studies, and the need for defining characteristics to inform non-HLA antibody pathogenicity. RECENT FINDINGS: Clinical studies continue to show associations between non-HLA antibodies and rejection and reduced allograft survival across multiple transplanted organ types. The worst clinical outcomes continue to be observed among recipients testing positive for both non-HLA and donor-specific HLA antibodies. Mechanistic insights from both animal and clinical studies support a model in which tissue injury accompanied by an inflammatory environment influence non-HLA antibody formation and pathogenicity. SUMMARY: Immune triggers that lead to non-HLA antibody formation and pathogenicity are complex and poorly understood. The ability of non-HLA antibodies to mediate allograft injury may depend upon their affinity and strength (titer), target specificity, density of the target antigen, and synergy with donor-specific HLA antibodies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".