Carlo Napolitano

Genetics of Long QT, Brugada, and Other Channelopathies

In the early 1990s, the seminal work of Keating and his group introduced the concept that inherited DNA abnormalities of the genes encoding cardiac ion channels create an arrhythmogenic substrate predisposing to sudden cardiac death. 1 2 3 Despite the focus of Keating and coworkers mainly on the long QT syndrome (LQTS), it soon became evident that genes encoding ion channels represent the genetic substrate for several other arrhythmogenic syndromes occurring in the structurally intact heart that are frequently labeled as “ idiopathic ventricular fibrillation.”

Markovian model for wild-type and mutant (Y1795C and Y1795H) human cardiac Na/sup +/ channel

Long QT syndrome (LQTS) and Brugada syndrome (BrS) are inherited syndromes predisposing to ventricular arrhythmias and sudden death. Emerging evidences related LQTS and BrS to dysfunctions of cardiac ion channels. Recently, two novel missense mutations in gene encoding for the cardiac Na channel have been identified (Y1795C for LQTS and Y1795H for BrS). Both mutations alter inactivation, intermediate inactivation, onset of inactivation of Na current and cause a sustained Na current. In this study we present a Markovian model of wild type and mutant Na channels. Model includes three closed states, an open state, and five inactivated states. Transition rates between these states were identified on the basis of electrophysiological experiments. The model is able to reproduce the current alterations observed in mutant channels just by alter the transition rates with respect to wild type assignment.

Clinical timelines development from textual medical reports in Italian

Patients diagnosed with chronic conditions are visited multiple times over the years. The medical reports produced during these visits often include valuable knowledge in the form of free text. To help physician access and review this knowledge, natural language processing and aggregation techniques are needed. In this work, we propose a system that extracts and summarizes information from medical reports written in Italian. For each patient, the system builds and visualizes a timeline of the extracted events. The proposed approach has the potential to enhance the process of reviewing patient clinical histories, reducing the time needed to access large amounts of data. In the future, an extension of the visualized timeline and an extrinsic evaluation will be performed.