PSEN1 and NUP98 as Diagnostic Biomarkers for Human Myocarditis
Notice bibliographique
Résumé
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 .
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».