Simone Furini

The study uses molecular dynamics simulations to show that wild-type hERG fails to conduct K+ and evolves toward an inactivated-like conformation, contrasting with the N629D mutant which maintains conductivity.

The paper estimates current-voltage characteristics of K+ channels using Markov State Models from MD simulations, showing agreement with direct simulations at low potentials.

The paper uses molecular dynamics simulations to show that both hard and soft knock-on mechanisms of ion conduction in potassium channels are accessible and reversible, depending on simulation parameters.

The paper compares genetic effects on disease susceptibility and progression, finding limited overlap and suggesting larger samples or different progression measures are needed.

The paper develops a method to build predictive Markov State Models (MSMs) of ion channel permeation using MD simulations, enabling accurate prediction of ion currents through the KcsA channel.

This paper improves a genetic score for predicting COVID-19 severity by incorporating organ-specific weights derived from Boolean quantum features and genetic algorithms.

The paper quantifies resource load from genetic constructs in mammalian cells and provides a framework for designing more efficient synthetic circuits.

This study combines common and rare variant analyses with single-cell multiomics to identify cell-type-specific genetic risk factors for severe COVID-19, revealing molecular mechanisms and potential therapeutic targets.

The paper shows that the L412F polymorphism in TLR3 is associated with severe COVID-19 in males by inhibiting autophagy and reducing TNF production.

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 a genetic variant in the SELP gene associated with increased thrombosis risk in male COVID-19 patients, suggesting potential for anti-P-selectin antibody therapy.

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 identifies rare TLR7 variants associated with severe COVID-19 in young males, suggesting a genetic basis for disease susceptibility.

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.

The paper identifies heat-shock promoters that activate rapidly in response to synthetic gene expression in E. coli and uses them to build a CRISPR/dCas9-based feedback system that improves protein yield by adjusting synthetic construct expression based on cellular burden.