Abstract 14361: Gene Expression Profiling for the Identification and Classification of Antibody-mediated Heart Rejection
Bibliographic record
Abstract
Introduction: Antibody-mediated rejection (AMR) of heart transplants is a major determinant of allograft loss. Improving our understanding of the pathophysiology, disease activity and disease stage in heart AMR is an unmet need. Hypothesis: Gene expression assessments may complement the current gold standard represented by histopathology. Methods: We prospectively monitored 617 heart transplant recipients referred from four French heart transplant centers for AMR. We compared patients with AMR to a matched control group of patients without AMR. We characterized all patients using histopathology (ISHLT 2013), immunostaining, circulating anti-HLA DSA and gene expression at the time of biopsy. The principal effector cells were also evaluated by in vitro human cell cultures. We studied an additional external validation cohort of heart recipients transplanted in Edmonton, Alberta Canada. Results: We included 208 heart transplant patients (110 in the test cohort 98 in the validation cohort) with 240 biopsies (98 pAMR ISHLT cases and 142 controls). The AMR selective gene sets discriminated patients with AMR from those without and included NK transcripts (AUC=0.87) (and selective changes in CD16A signaling and IFNG-inducible genes), endothelial activation transcripts (AUC=0.80), macrophage transcripts (AUC=0.86) and transcripts involved in the IFNG response (AUC=0.84, p Conclusions: Antibody-mediated heart rejection is mainly driven by the NK burden, endothelial activation, macrophage burden and IFNG effects. Molecular intragraft measurements for these specific pathogenesis-based transcripts classify AMR with great accuracy, reclassify pAMR1 cases, and correlate with the degree of injury and disease activity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".