The molecular phenotypes of rejection in kidney transplant biopsies
Bibliographic record
Abstract
PURPOSE OF REVIEW: The recent emergence of a system for distinguishing T-cell-mediated rejection (TCMR) from antibody-mediated rejection (ABMR), including C4d-negative ABMR, allows us to map the molecular features of these conditions. RECENT FINDINGS: The TCMR landscape is dominated by molecules expressed in effector T cells, antigen-presenting cells (macrophages, dendritic cells, B cells) and interferon-gamma (IFNG)-induced genes. A surprising finding is the association of transcripts for inhibitory molecules such as CTLA4 and PDL1 with TCMR, indicating that this tubulo-interstitial inflammatory compartment is actively controlled. ABMR is dominated by endothelial transcripts related to angiogenesis, reflecting endothelial injury; natural killer (NK)-cell transcripts; and selected IFNG-regulated transcripts. This suggests a cognate unit of NK cells engaging donor-specific antibody bound to donor human leukocyte antigen antigens through their CD16a (FCGR3A) Fc receptors, triggering IFNG release. TCMR and ABMR share many rejection-associated transcripts, mainly IFNG-induced genes and transcripts shared between NK cells and CD8 effector T cells (e.g., KLRD1). In addition, acute kidney injury transcripts, which reflect the parenchymal response to injury, are shared between different forms of rejection and are indicative of disease progression. SUMMARY: Microarray assessment provides a new dimension in biopsy assessment for diagnosis that offers mechanistic insights and sometimes challenges histology assessments.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".