Climate-responsive design practices: A transdisciplinary methodology for achieving sustainable development goals in cultural and natural heritage - Elena Lucchi; Francesca Turati; Benedetta Colombo; Eva Schito (Journal of Cleaner Production, 2024)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

A Glutamate Transporter EAAT1 Gene Variant Influences Amygdala Functional Connectivity in Bipolar Disorder - Sara Poletti; Martina Riberto; Benedetta Vai; Davide Ghiglino; Cristina Lorenzi; Alice Vitali; Silvia Brioschi; Clara Locatelli; Alessandro Serretti; Cristina Colombo; Francesco Benedetti (Journal of Molecular Neuroscience, 2018)

Stem Cell Factor (SCF) is a putative biomarker of antidepressant response - Francesco Benedetti; Sara Poletti; Thomas A. Hoogenboezem; Clara Locatelli; Oliver Ambrée; Harm de Wit; Annemarie J. M. Wijkhuijs; Elena Mazza; Chiara Bulgarelli; Benedetta Vai; Cristina Colombo; Enrico Smeraldi; et al. (Journal of Neuroimmune Pharmacology, 2016)

Lithium and GSK-3β promoter gene variants influence cortical gray matter volumes in bipolar disorder - Francesco Benedetti; Sara Poletti; Daniele Radaelli; Clara Locatelli; Adele Pirovano; Cristina Lorenzi; Benedetta Vai; Irene Bollettini; Andrea Falini; Enrico Smeraldi; Cristina Colombo (Psychopharmacology, 2015)