Identification of features and differences in PD-1 inhibitor-associated myocarditis and acute myocardial infarction using proteomic analysis: a clinical and preclinical study
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs)-related myocarditis, a severe complication characterized by elevated cardiac troponin I, poses significant clinical challenges in distinguishing it from acute myocardial infarction (AMI). Our study aimed to identify plasma protein biomarkers that differentiate ICIs-myocarditis from AMI. Plasma samples from 5 ICIs-myocarditis patients (with paired baseline and diagnosis samples) and 5 angiography-confirmed AMI patients, matched for age, gender, smoking history, and pre-existing heart disease, were analyzed using label-free liquid chromatography-mass spectrometry proteomics. A total of 1521 plasma proteins were identified, with 1325 quantifiable. Proteomic profiling revealed differentially expressed proteins (DEPs) in ICIs-myocarditis associated with myocardial contraction, proteasome activity, NF-κB signaling, immunoregulation, and amino acid metabolism. Through validation in animal models of ICIs-myocarditis and AMI, two plasma proteins-MYOM3 (myomesin 3) and galectin-1 (LGALS1)-were identified as potential biomarkers linked to the onset of ICIs-related myocarditis. Further validation using expanded clinical cohorts confirmed their differential expression. These findings highlight MYOM3 and galectin-1 as promising biomarkers for distinguishing ICIs-related myocarditis from AMI, providing insights for clinical diagnosis and mechanistic research into immune-related cardiotoxicity.
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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 | 0.000 |
| 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) | 0.001 | 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".