Molecular diagnosis of ABMR with or without donor-specific antibody in kidney transplant biopsies: Differences in timing and intensity but similar mechanisms and outcomes
Bibliographic record
Abstract
We studied the clinical, histologic, and molecular features distinguishing DSA-negative from DSA-positive molecularly defined antibody-mediated rejection (mABMR). We analyzed mABMR biopsies with available DSA assessments from the INTERCOMEX study: 148 DSA-negative versus 248 DSA-positive, compared with 864 no rejection (excluding TCMR and Mixed). DSA-positivity varied with mABMR stage: early-stage (EABMR) 56%; fully developed (FABMR) 70%; and late-stage (LABMR) 58%. DSA-negative patients with mABMR were usually sensitized, 60% being HLA antibody-positive. Compared with DSA-positive mABMR, DSA-negative mABMR was more often C4d-negative; earlier by 1.5 years (average 2.4 vs. 3.9 years); and had lower ABMR activity and earlier stage in molecular and histology features. However, the top ABMR-associated transcripts were identical in DSA-negative versus DSA-positive mABMR, for example, NK-associated (e.g., KLRD1 and GZMB) and IFNG-inducible (e.g., PLA1A). Genome-wide class comparison between DSA-negative and DSA-positive mABMR showed no significant differences in transcript expression except those related to lower intensity and earlier time of DSA-negative ABMR. Three-year graft loss in DSA-negative mABMR was the same as DSA-positive mABMR, even after adjusting for ABMR stage. Thus, compared with DSA-positive mABMR, DSA-negative mABMR is on average earlier, less active, and more often C4d-negative but has similar graft loss, and genome-wide analysis suggests that it involves the same mechanisms. SUMMARY SENTENCE: In 398 kidney transplant biopsies with molecular antibody-mediated rejection, the 150 DSA-negative cases are earlier, less intense, and mostly C4d-negative, but use identical molecular mechanisms and have the same risk of graft loss as the 248 DSA-positive cases.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".