Linking conventional activated sludge treatment plant performances for micropollutants removal to environmental risk and SimpleTreat model assessment - C. Di Marcantonio; A. Chiavola; D. Spagnoli; B. Meşe; F. Margarita; V. Gioia; A. Frugis; S. Leoni; G. Cecchini; M. Spizzirri; M. R. Boni (International Journal of Environmental Science and Technology, 2024)
The attention to the contaminants of emerging concerns or micropollutants is continuously rising, also stimulated by the new initiatives at European level. The present study focuses on the removal of target micropollutants in a conventional activated sludge wastewater treatment plant which is the most common system and considered a source of micropollutants to the environment. The aims were to evaluate the removal efficiency of different stages of the wastewater treatment plant and to conduct the Environmental Risk Assessment on the final effluent. As indicated by the European’s guidelines on Environmental Risk Assessment, the SimpleTreat4.1 model was applied to this purpose: different values of the biodegradation constant, which represents the key factor for modelling, were applied, and the effects on the quality of prediction were evaluated. The evaluations were carried out thorough 3-year monitoring activity in a full-scale wastewater treatment plant on 10 micropollutants belonging to the classes of illicit drugs, pharmaceuticals and psychoactive. The model was found statistically not satisfactory for most of the investigated micropollutants. A better prediction was obtained when the biodegradation constant was calculated based on the full-scale data. Nonetheless, the results obtained in terms of risk were considered acceptable and comparable among each other. Graphical abstract

Detection and full genomic sequencing of rare hepatitis E virus genotype 4d in Italian wastewater, undetected by clinical surveillance - C. Veneri; D. Brandtner; P. Mancini; G. Bonanno Ferraro; M. Iaconelli; C. Del Giudice; A.R. Ciccaglione; R. Bruni; M. Equestre; C. Marcantonio; E. Suffredini; G. La Rosa (Science of The Total Environment, 2024)

Assessment of potential selection bias in neuroimaging studies of postoperative delirium and cognitive decline: lessons from the SAGES study - Michele Cavallari; Tamara G. Fong; Alexandra Touroutoglou; Bradford C. Dickerson; Eva Schmitt; Thomas G. Travison; Edward R. Marcantonio; Long H. Ngo; Towia Libermann; Alvaro Pascual-Leone; Mouhsin M. Shafi; Sharon K. Inouye; et al. (Brain Imaging and Behavior, 2022)

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.

Correction to: Genetic Diversity Among Genogroup II Noroviruses and Progressive Emergence of GII.17 in Wastewaters in Italy (2011–2016) Revealed by Next-Generation and Sanger Sequencing - E. Suffredini; M. Iaconelli; M. Equestre; B. Valdazo-González; A. R. Ciccaglione; C. Marcantonio; S. Della Libera; F. Bignami; G. La Rosa (Food and Environmental Virology, 2018)

Genetic Diversity Among Genogroup II Noroviruses and Progressive Emergence of GII.17 in Wastewaters in Italy (2011–2016) Revealed by Next-Generation and Sanger Sequencing - E. Suffredini; M. Iaconelli; M. Equestre; B. Valdazo-González; A. R. Ciccaglione; C. Marcantonio; S. Della Libera; F. Bignami; G. La Rosa (Food and Environmental Virology, 2018)

The role of PKCε-dependent signaling for cardiac differentiation - D. Galli; G. Gobbi; C. Carrubbi; D. Marcantonio; L. Benedetti; M. G. C. De Angelis; T. Meschi; M. Vaccarezza; M. Sampaolesi; P. Mirandola; M. Vitale (Histochemistry and Cell Biology, 2013)

Activation of endoplasmic reticulum stress response by hepatitis C virus proteins - A. R. Ciccaglione; A. Costantino; E. Tritarelli; C. Marcantonio; M. Equestre; N. Marziliano; M. Rapicetta (Archives of Virology, 2005)