HLA-DR and -DQ Eplet Mismatches and Transplant Glomerulopathy: A Nested Case–Control Study
Bibliographic record
Abstract
We conducted a nested case-control study from a cohort of adult kidney transplant recipients to assess the risk of transplant glomerulopathy (TG) as a function of donor and recipient HLA-DR and -DQ incompatibility at the eplet level. Cases (n = 52) were defined as patients diagnosed with transplant glomerulopathy based on biopsies showing glomerular basement membrane duplication without immune complex deposition. Controls (n = 104) with a similar follow-up from transplantation were randomly selected from the remaining cohort. HLAMatchmaker was used to ascertain the number of DRB1/3/4/5, DQA1 and DQB1 related eplet mismatches (eplet load). Multivariable conditional logistic regression models demonstrated an increase in the odds of TG (odds ratios [OR] of 2.84 [95% confidence interval (CI): 1.03, 7.84] and 4.62 [95% CI: 1.51, 14.14]) in the presence of 27-43 and >43 HLA-DR + DQ related eplet mismatches versus <27 eplet mismatches, respectively. When the eplet load was modeled as a continuous variable, the OR for TG was 1.25 (95% CI: 1.04, 1.50) for every 10 additional HLA-DR + DQ eplet mismatches. Our study suggests that minimization of HLA-DR + DQ eplet mismatches may decrease the incidence of transplant glomerulopathy diagnosed by indication biopsies. The role of eplet immunogenicity/antigenicity as determinants of allograft outcomes requires further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".