Natural killer cell functional genetics and donor-specific antibody-triggered microvascular inflammation
Bibliographic record
Abstract
Antibody-mediated rejection (ABMR) is a leading cause of graft failure. Emerging evidence suggests a significant contribution of natural killer (NK) cells to microvascular inflammation (MVI). We investigated the influence of genetically determined NK cell functionality on ABMR development and activity. The study included 86 kidney transplant recipients subjected to systematic biopsies triggered by donor-specific antibody detection. We performed killer immunoglobulin-like receptor typing to predict missing self and genotyped polymorphisms determining NK cell functionality ( FCGR3A V/F158 [rs396991], KLRC2 wt/del , KLRK1 HNK/LNK [rs1049174], rs9916629-C/T). Forty-four patients had ABMR with considerable MVI and elevated NK cell transcripts. Missing self was not related to MVI. Only KLRC2 wt/wt showed an association (MVI score: 2 [median; interquartile range: 0-3] versus 0 [0-1] in KLRC2 wt/del recipients; p=0.001) and remained significant in a proportional odds multivariable model (odds ratio [OR]: 7.84, 95% confidence interval [CI]: 2.37-30.47, p=0.001). A sum score incorporating all polymorphisms and missing self did not outperform a score including only KLRC2 and FCGR3A variants, which were predictive in univariable analysis. NK cell genetics did not affect graft functional decline and survival. In conclusion, a functional KLRC2 polymorphism emerged as an independent determinant of ABMR activity, without a considerable contribution of missing self and other NK cell gene polymorphisms.
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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 | 0.001 | 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) | 0.001 | 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".