Unveiling genetic insights: Array-CGH and WES discoveries in a cohort of 122 children with essential autism spectrum disorder.

Background: Autistic Spectrum Disorder (ASD) is a neurodevelopmental disorder with a strong genetic component and high heterogeneity. Essential ASD refers to patients who do not have other comorbidities. This study aimed to investigate the genetic basis of essential ASD using whole exome sequencing (WES) and array-comparative genomic hybridization (array-CGH).

Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.

Background And Objective: Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decision-making processes in the treatment of this malignancy. Though several machine learning models analyzing both clinical and histopathological information have been developed in literature to address this task, these approaches turned out to be unsuitable for describing this problem.

Methods: In this study, we designed a novel artificial intelligence-based approach which converts clinical information into an image-form to be analyzed through Convolutional Neural Networks.

Feasibility, safety and clinical impact of a less-invasive totally-endovascular (LITE) technique for transfemoral TAVI: A 1000 patients single-centre experience.

Background: Trans-femoral (TF) represents the main access for TAVI. Although there are various technical strategies to conduct TF-TAVI (pacing modality, secondary arterial access, primary access puncture etc.), the optimal technique is not recognized.