Exploring the cardiac response to injury in heart transplant biopsies
Bibliographic record
Abstract
BACKGROUND: Because injury is universal in organ transplantation, heart transplant endomyocardial biopsies present an opportunity to explore response to injury in heart parenchyma. Histology has limited ability to assess injury, potentially confusing it with rejection, whereas molecular changes have potential to distinguish injury from rejection. Building on previous studies of transcripts associated with T cell-mediated rejection (TCMR) and antibody-mediated rejection (ABMR), we explored transcripts reflecting injury. METHODS: Microarray data from 889 prospectively collected endomyocardial biopsies from 454 transplant recipients at 14 centers were subjected to unsupervised principal component analysis and archetypal analysis to detect variation not explained by rejection. The resulting principal component and archetype scores were then examined for their transcript, transcript set, and pathway associations and compared to the histology diagnoses and left ventricular function. RESULTS: Rejection was reflected by principal components PC1 and PC2, and by archetype scores S2TCMR, and S3ABMR, with S1normal indicating normalness. PC3 and a new archetype score, S4injury, identified unexplained variation correlating with expression of transcripts inducible in injury models, many expressed in macrophages and associated with inflammation in pathway analysis. S4injury scores were high in recent transplants, reflecting donation-implantation injury, and both S4injury and S2TCMR were associated with reduced left ventricular ejection fraction. CONCLUSION: Assessment of injury is necessary for accurate estimates of rejection and for understanding heart transplant phenotypes. Biopsies with molecular injury but no molecular rejection were often misdiagnosed rejection by histology.TRAIL REGISTRATION. ClinicalTrials.gov NCT02670408FUNDING. Roche Organ Transplant Research Foundation, the University of Alberta Hospital Foundation, and Alberta Health Services.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".