Biomarker discovery in cardiac allograft vasculopathy using targeted aptamer proteomics
Bibliographic record
Abstract
Abstract Cardiac allograft vasculopathy (CAV) limits long‐term survival after heart transplantation. Non‐invasive evaluation is challenging, and currently, there is no validated biomarker for CAV diagnosis or prognostication. To identify potential candidate CAV biomarkers, we utilized the Slow Off‐rate Modified Aptamer (SOMAscan) assay, which evaluates over 1000 serum proteins, including many relevant to biological pathways in CAV. We evaluated three heart transplant patient groups according to angiographic ISHLT CAV grade: CAV 1‐2 (mild‐moderate CAV), CAV 3 (severe CAV), and CAV 0 (normal control). SOMAscan assays were performed and proteins quantitated. Comparisons of proteins between study groups were performed using one‐way ANOVA (false discovery rate q ‐value < 0.10). Thirty‐one patients (12 mild‐moderate CAV, 9 severe CAV, 10 controls) were included: 81% male, median age 57 years and median 1.1 years post‐transplant. Compared to controls, patients with mild‐moderate CAV had similar characteristics, while patients with severe CAV had longer time from transplant and increased allosensitization. Statistical/bioinformatics analysis identified 14 novel biomarkers for CAV, including 4 specific for mild‐moderate CAV. These proteins demonstrated important actions including apoptosis, inflammation, and platelet/coagulation activation. Upon preliminary receiver operating characteristics curve analysis, our protein biomarkers showed moderate‐to‐high discriminative ability for CAV (area under curve: 0.72 to 0.94). These candidate biomarkers are being validated in prospective studies.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".