Ziprasidone triggers inflammasome signaling via PI3K-Akt-mTOR pathway to promote atrial fibrillation.

BACKGROUND: Second-generation antipsychotics increase the risk of atrial fibrillation. This study explores whether the atypical antipsychotic ziprasidone triggers inflammasome signaling, leading to atrial arrhythmia. METHODS: Electromechanical and pharmacological assessments were conducted on the rabbit left atria (LA). The patch-clamp technique was used to measure ionic channel currents in single cardiomyocytes. Detection of cytosolic reactive oxygen species production was performed in atrial cardiomyocytes. RESULTS: The duration of action potentials at 50 % and 90 % repolarization was dose-dependently shortened in ziprasidone-treated LA. Diastolic tension in LA increased after ziprasidone treatment. Ziprasidone-treated LA showed rapid atrial pacing (RAP) triggered activity. PI3K inhibitor, Akt inhibitor and mTOR inhibitor abolished the triggered activity elicited by ziprasidone in LA. The NLRP3 inhibitor MCC950 suppressed the ziprasidone-induced post-RAP-triggered activity. MCC950 treatment reduced the reverse-mode Na+/Ca2+ exchanger current in ziprasidone-treated myocytes. Cytosolic reactive oxygen species production decreased in ziprasidone-treated atrial myocytes after MCC950 treatment. Protein levels of inflammasomes and proinflammatory cytokines, including NLRP3, caspase-1, IL-1ß, IL-18, and IL-6 were observed to be upregulated in myocytes treated with ziprasidone. CONCLUSIONS: Our findings suggest ziprasidone induces atrial arrhythmia, potentially through upregulation of the NLRP3 inflammasome and enhancement of reactive oxygen species production via the PI3K/Akt/mTOR pathway.

Machine learning for prediction of schizophrenia based on identifying the primary and interaction effects of minor physical anomalies.

In the neurodevelopmental model of schizophrenia, minor physical anomalies (MPAs) are considered neurodevelopmental markers of schizophrenia. To date, there has been no research to evaluate the interaction between MPAs. Our study built and used a machine learning model to predict the risk of schizophrenia based on measurements of MPA items and to investigate the potential primary and interaction effects of MPAs. The study included 470 patients with schizophrenia and 354 healthy controls. The models used are classical statistical model, Logistic Regression (LR), and machine leaning models, Decision Tree (DT) and Random Forest (RF). We also plotted two-dimensional scatter diagrams and three-dimensional linear/quadratic discriminant analysis (LDA/QDA) graphs for comparison with the DT dendritic structure. We found that RF had the highest predictive power for schizophrenia (Full-training AUC = 0.97 and 5-fold cross-validation AUC = 0.75). We identified several primary MPAs, such as the mouth region, high palate, furrowed tongue, skull height and mouth width. Quantitative MPA analysis indicated that the higher skull height and the narrower mouth width, the higher the risk of schizophrenia. In the interaction, we further identified that skull height and mouth width, furrowed tongue and skull height, high palate and skull height, and high palate and furrowed tongue, showed significant two-item interactions with schizophrenia. A weak three-item interaction was found between high palate, skull height, and mouth width. In conclusion, we found that the two machine learning methods showed good predictive ability in assessing the risk of schizophrenia using the primary and interaction effects of MPAs.