PSEN1 and NUP98 as Diagnostic Biomarkers for Human Myocarditis
Bibliographic record
Abstract
Background Myocarditis, inflammation of the myocardium not associated with ischemia, is a spectrum of conditions causing considerable morbidity and mortality. The etiologies known to drive such inflammation are diverse and include autoimmunity and drug hypersensitivity, but are most commonly attributed to cardiotropic viral infections. Clinical symptoms are also variable, ranging from life threatening acute illness to chronic disease, while others never come to clinical attention. Moreover, these factors make the frequency of myocarditis difficult to ascertain, however, an estimated 9% of adult autopsies show myocarditis on histologic examination. Given the tools presently available, clinical, etiologic and hitstologic variability make diagnosis, and therefore treatment, exceedingly difficult. The current gold standard of diagnosis is inflammation shown on endomyocardial biopsy with (“active”) or without (“borderline”) myocyte damage. However, under these criteria, myocarditis diagnostic sensitivity is estimated as low as 30%. To improve upon this, we examined several markers implicated in the pathogenesis of viral myocarditis in animal models as possible adjunct diagnostic biomarkers in human myocarditis. PSEN1, a cellular protease implicated in heart failure and NUP98, a nuclear pore protein with inducible cardioprotective and antiviral gene transcription capabilities, have emerged as promising candidates. Design Two groups were examined for PSEN1 and NUP98 immunohistochemical (IHC) staining: a development set of 50 cases (18 lymphocytic active or healing myocarditis, 4 eosinophilic myocarditis, 4 idiopathic dilated cardiomyopathy, 3 hypertrophic cardiomyopathy, 4 sarcoidosis, 3 transplant rejection, 1 toxoplasmosis, 4 arrhythmogenic right ventricular cardiomyopathy (ARVC), 4 coronary artery disease (CAD) and 5 normal controls) and a validation set of all (62) cardiac biopsies performed at SPH from January 2015–June 2016, irrespective of diagnosis. Staining intensity was assessed by computer aided image analysis. Statistical analysis was performed using Mann Whitney U test and receiver operating characteristic (ROC) curves. All protocols were approved by the UBC/PHCRI Research Ethics Board. Results PSEN1 distinguished myocarditis from all other diagnoses in the development set (p=0.0001). NUP98 could distinguish inflammatory myocarditides as well as viral from non‐viral from most other diagnoses in the development set (p=0.001). ROC values comparing myocarditis to all other cardiomyopathies was 0.85 for PSEN1 and 0.80 for NUP98. These findings appear hold true in preliminary analyses of the validation set . Conclusion PSEN1 and NUP98 appear to be valuable biomarkers, particularly in combination, for improving sensitivity of endomyocardial biopsy for diagnosing myocarditis, and may provide greater ability to deduce etiologic information from such biopsies. Moreover, PSEN1 and NUP98 are detectable even in regions even without inflammation and in tissue long after initial insult. These insights will aid in personalization of treatment and significantly improve clinical outcomes. Support or Funding Information This research is funded by the Providence Health Care Research Institute and through donations made to the St. Paul's Hospital Foundation. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".