The Molecular Microscope Diagnostic System: Assessment of Rejection and Injury in Heart Transplant Biopsies
Bibliographic record
Abstract
This review describes the development of the Molecular Microscope Diagnostic System (MMDx) for heart transplant endomyocardial biopsies (EMBs). MMDx-Heart uses microarrays to measure biopsy-based gene expression and ensembles of machine learning algorithms to interpret the results and compare each new biopsy to a large reference set of earlier biopsies. MMDx assesses T cell-mediated rejection (TCMR), antibody-mediated rejection (AMR), recent parenchymal injury, and atrophy-fibrosis, continually "learning" from new biopsies. Rejection-associated transcripts mapped in kidney transplants and experimental systems were used to identify TCMR, AMR, and recent injury-induced inflammation. Rejection and injury emerged as gradients of intensity, rather than binary classes. AMR was one-third donor-specific antibody (DSA)-negative, and many EMBs first considered to have no rejection displayed minor AMR-like changes, with increased probability of DSA positivity and subtle inflammation. Rejection-associated transcript-based algorithms now classify EMBs as "Normal," "Minor AMR changes," "AMR," "possible AMR," "TCMR," "possible TCMR," and "recent injury." Additionally, MMDx uses injury-associated transcript sets to assess the degree of parenchymal injury and atrophy-fibrosis in every biopsy and study the effect of rejection on the parenchyma. TCMR directly injures the parenchyma whereas AMR usually induces microcirculation stress but relatively little initial parenchymal damage, although slowly inducing parenchymal atrophy-fibrosis. Function (left ventricular ejection fraction) and short-term risk of failure are strongly determined by parenchymal injury. These discoveries can guide molecular diagnostic applications, either as a central MMDx system or adapted to other platforms. MMDx can also help calibrate noninvasive blood-based biomarkers to avoid unnecessary biopsies and monitor response to therapy.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".