Editorial: A hill of needs: Non-HLA antibodies and transplantation
Bibliographic record
Abstract
A hill of needs: Current state of the art and knowledge gaps for non- HLA antibodies in allograft transplantationIn recent years a growing body of evidence suggests a relationship between the presence of non-HLA antibodies and graft loss and/or rejection episodes after organ transplant (1-3).Non-HLA antibodies are divided into those with specificity for alloantigens, such as the polymorphic alloantigen major histocompatibility class-1 chain A (MICA), and those with specificity for a range of autoantigens such as the angiotensin II type 1 receptor (AT1R), K-alpha 1 tubulin, or cardiac myosin.But, the diversity in form and function of non-HLA antibodies extends beyond this initial distinction.Non-HLA antibodies recognize antigens found in various cellular compartments including the cell membrane, intracellular spaces, and secreted extracellular vesicles, and further, the formation and likely the function of non-HLA antibodies is multimechanistic.While, the data indicate that detection of non-HLA antibodies may be useful in identifying patients at risk for allograft injury or graft loss in organ specific contexts, thus far no human studies have proven that non-HLA antibodies can directly contribute to the pathogenesis of graft dysfunction.Non-HLA antibodies can be detected using commercial reagents or with laboratory-developed tests.However, lacking are reference sera containing defined concentrations of non-HLA antibodies, and therefore, the definition of a positive result may be based on thresholds that lack clinical relevance.The combination of these factors lead to technical variation between studies, and as such there is no one standard assay in the field that defines the impact of non-HLA antibodies on graft loss.A complete overview on the detection of non-HLA antibodies by solid-phase (ELISA, Luminex) and cell-based (culture, flow-cytometry) techniques is provided by the state-of-the-art review written by Lammerts et al..
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".