Donor and recipient human leukocyte antigen-G polymorphisms modulate the risk of adverse immunologic events following lung transplantation
Bibliographic record
Abstract
The long-term benefits of lung transplantation (LTx) are limited by pathogenic alloimmune responses that drive injury, inflammation, and chronic dysfunction. Human leukocyte antigen-G (HLA-G) plays a key role in the modulation of these pathways. This study assesses the impact of the HLA-G genotype on immunologic risk and survival following LTx. This retrospective cohort study included 289 bilateral LTx. Recipient and donor HLA-G genotypes were analyzed to identify associations with de novo donor-specific antibodies, acute rejection, chronic lung allograft dysfunction, and allograft survival. We further assessed these associations, both individually and in paired analysis, based on a grouped haplotype classification of HLA-G expression. Donor HLA-G single nucleotide polymorphisms were associated with allograft injury, the onset of chronic lung allograft dysfunction following injury, and allograft survival. Recipient HLA-G single nucleotide polymorphisms were associated with allograft injury, cellular rejection, and donor-specific antibody formation. "Low HLA-G expression" donor haplotypes were associated with impaired allograft survival, as were "low HLA-G expression" donor-recipient haplotype pairs. This study provides compelling evidence for the role of HLA-G in modulating immunologic risk after LTx. Our results highlight the importance of both donor and recipient HLA-G genotypes on the overall risk profile and underscore the lasting influence of donor genotype on lung transplant outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".