Mirella Bruttini

The paper validates the NovaSeq6000 RUO platform with automated library preparation for clinical-grade whole-exome sequencing, showing equivalence to the CE-IVD certified system.

The paper develops a machine learning model to predict COVID-19 severity using exonic variants from whole exome sequencing data, finding that variants across all frequency categories contribute to severity.

The paper identifies shorter androgen receptor polyQ alleles as protective against severe COVID-19 in males using machine learning on whole-exome sequencing data from the GEN-COVID cohort.

The paper describes a biobanking initiative collecting genetic and clinical data from over 1,000 SARS-CoV-2-positive individuals to study multi-organ involvement and genetic factors in COVID-19 severity.

The paper proposes a post-Mendelian genetic model integrating common and rare variants to predict severe vs. mild COVID-19 outcomes in an Italian cohort.

The paper reports Whole Exome Sequencing analysis of 35 hospitalized COVID-19 patients, identifying pathogenic mutations linked to virus infection susceptibility and disease progression.

The paper identifies ACE2 gene variants associated with interindividual variability in COVID-19 susceptibility in the Italian population using whole-exome-sequencing data.