Functional Fc gamma receptor gene polymorphisms and donor-specific antibody-triggered microcirculation inflammation
Bibliographic record
Abstract
Fc-dependent effector mechanisms may contribute to antibody-mediated rejection (ABMR), and distinct gene polymorphisms modifying the function of Fc gamma receptors (FcγRs) may influence the capability of donor-specific antibodies (DSAs) to trigger inflammation. To evaluate the relevance of functional FcγR variants in late ABMR, 85 DSA-positive kidney allograft recipients, who were recruited upon antibody screening of 741 prevalent patients, were genotyped for polymorphisms in FcγRIIA ( FCGR2A -H/R 131 ; rs1801274), FcγRIIIA ( FCGR3A -V/F 158 ; rs396991), and FcγRIIIB ( FCGR3B -neutrophil antigen 1 ([NA1]/NA2; rs35139848). Individuals with high-affinity FCGR3A -V 158 alleles (V/V 158 or V/F 158 ) showed a higher rate (and extent) of peritubular capillaritis (ptc) in protocol biopsies than homozygous carriers of the lower-affinity allele (ptc score ≥1: 53.6% vs 25.9%; P = .018). Associations were independent of C1q-binding to DSA or capillary C4d. In parallel, there was a trend toward increased macrophage- and injury-repair response–associated transcript subsets. Kidney function over 24 months, however, was not different. In support of a functional role of FcγRIIIA polymorphism, NK92 cells expressing FCGR3A -V 158 produced >2 times as much interferon gamma upon incubation with HLA antibody-coated cells as those expressing FCGR3A -F 158 . FcγRIIA and FcγRIIIB polymorphisms were not associated with allograft morphology. Our data suggest that the presence of high-affinity FcγRIIIA variants may favor DSA-triggered microcirculation inflammation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".