The molecular phenotypes of rejection in kidney transplant biopsies
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it