Endothelial transcripts uncover a previously unknown phenotype: C4d-negative antibody-mediated rejection
Bibliographic record
Abstract
PURPOSE OF REVIEW: In the last decade, there has been a growing recognition of alloantibody responses in organ transplantation, but phenotypes related to antibody-mediated rejection (ABMR) remain incompletely defined. This article reviews recent molecular studies in kidney allograft tissues that decipher molecular burden and mechanisms of ABMR, leading to discovery of a new phenotype: 'C4d-negative ABMR'. RECENT FINDINGS: High endothelial gene expression in kidney transplant biopsies with anti-human leukocyte antigen alloantibody indicates active antibody-mediated damage and poor graft survival, defining a previously unknown group of C4d-negative ABMR. C4d-negative ABMR is characterized by high intragraft endothelial gene expression, alloantibodies, histology of chronic ABMR (less frequently acute ABMR), and poor outcomes. Thus, endothelial molecular phenotype in biopsies with circulating antibody detects degree of active graft injury, and many of these transcripts reflect endothelial activation. C4d-negative ABMR is twice as common as C4d-positive ABMR. Recognition of this new phenotype reveals ABMR (C4d positive or negative) as the most common cause of late kidney transplant loss. SUMMARY: C4d staining, although very useful, is insensitive for detecting ABMR. Measuring endothelial gene expression in biopsies from kidneys with alloantibody is a sensitive and specific method to diagnose ABMR and predict graft 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".