Fenoaltea in the Italian Mirror: Recollections by a Student of His

Internationally renowned as a brilliant researcher, Stefano Fenoaltea was also an outstanding teacher of economic history and economics. In this brief contribution, a former student of his during his latest years in the University of Rome “Tor Vergata”, who was later tutored by him as a Fondazione Einaudi Fellow, share his memories and impressions of Fenoaltea as a teacher and mentor

Il fascismo ‘liberista' e la ‘quasi abolizione' dell'imposta di successione del 1923

In summer 1923, in the midst of the ‘fight’ to balance the budget, Minister Alberto De Stefani announced the abolition of inheritance tax, pursuant to the ‘full powers’ granted to the government by the Parliament. This abolition – possibly the most iconic act of the ‘financial restauration’ carried on by De Stefani – provoked surprise and interest in the country and abroad but was substantially overlooked by historians. This chapter – first outcome of a research in progress – offers a first historical reconstruction of this episode of early 1920s Italian economic history, by documenting both the positions of an influent advisor of De Stefani, the economist Maffeo Pantaleoni, and even more, the lobbying activity carried on by pressure groups such as the bankers’ association, an influential businessman linked to Mussolini such as Cesare Goldmann, and a young, very proactive association of notaries. Moreover, the chapter surveys the way in which both Italian and international media reported on this case of politics of inequality, offering a different perspective on a crucial period in the consolidation of Fascist power

Fiscal Sources and the Distribution of Income in Italy: The Italian Historical Taxpayers' Database

This paper documents the ongoing construction of the Italian Historical Taxpayers’ Database (IHTD), currently consisting of 1,593,563 micro-records of income declarations filed by Italian autonomous workers in 1889, 1922 and 1933. Such a database results from the digitisation of a so far overlooked source, the printed lists of taxpayers’ declarations for some categories of the Imposta di ricchezza mobile, the most important Italian direct tax on income until the early 1970s. To contribute to the ‘rediscovery’ of these sources (and Italian fiscal sources in general), the paper surveys the history of the taxpayers’ lists in post-unification Italy, as well as the ‘classic’ arguments against the reliability of fiscal sources, in the light of available evidence. This makes possible to discuss how, while inevitably affected by biases and limitations, these sources offer historians a new perspective on the incomes of important social groups, and do so with an unparalleled level of granularity in terms of activities, geography, and gender, contributing in this way to the history of inequality during the Fascist period, and potentially to the broader economic history of post-unification Italy

La lutte des ouvriers de GKN à Florence, entre auto-organisation ouvrière et mobilisation sociale

Cette chronique décrit la lutte des travailleurs de l’usine GKN de Florence contre sa fermeture et sa délocalisation. Après avoir rendu compte des principales étapes du conflit, l’article se penche sur trois aspects fondamentaux : l’organisation syndicale interne à l’usine et sa capacité à renforcer les ressources militantes autonomes des travailleurs, l’habileté de ces derniers à mobiliser le tissu social environnant et à nouer des alliances avec d’autres mouvements sociaux et, enfin, la contribution apportée par le monde de l’université et de la recherche dans l’élaboration d’un plan de reconversion du site

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

The impact of common and rare variants in COVID-19 host genetics has been widely studied. In particular, in Fallerini et al. (Human genetics, 2022, 141, 147–173), common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. First, variants were converted into 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. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score (IPGS), which offers a very simple description of the contribution of host genetics in COVID-19 severity. IPGS leads to an accuracy of 55%–60% on different cohorts, and, after a logistic regression with both IPGS and age as inputs, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using not only the most informative Boolean features with respect to the genetic bases of severity but also the information on host organs involved in the disease. In this study, we generalize the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into “Boolean quantum features,” inspired by quantum mechanics. The organ coefficients were set via the application of the genetic algorithm PyGAD, and, after that, we defined two new integrated polygenic scores ((Formula presented.) and (Formula presented.)). By applying a logistic regression with both IPGS, ((Formula presented.) (or indifferently (Formula presented.)) and age as inputs, we reached an accuracy of 84%–86%, thus improving the results previously shown in Fallerini et al. (Human genetics, 2022, 141, 147–173) by a factor of 10%

Ultra-rare RTEL1 gene variants associate with acute severity of COVID-19 and evolution to pulmonary fibrosis as a specific long COVID disorder

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a novel coronavirus that caused an ongoing pandemic of a pathology termed Coronavirus Disease 19 (COVID-19). Several studies reported that both COVID-19 and RTEL1 variants are associated with shorter telomere length, but a direct association between the two is not generally acknowledged. Here we demonstrate that up to 8.6% of severe COVID-19 patients bear RTEL1 ultra-rare variants, and show how this subgroup can be recognized. Methods: A cohort of 2246 SARS-CoV-2-positive subjects, collected within the GEN-COVID Multicenter study, was used in this work. Whole exome sequencing analysis was performed using the NovaSeq6000 System, and machine learning methods were used for candidate gene selection of severity. A nested study, comparing severely affected patients bearing or not variants in the selected gene, was used for the characterisation of specific clinical features connected to variants in both acute and post-acute phases. Results: Our GEN-COVID cohort revealed a total of 151 patients carrying at least one RTEL1 ultra-rare variant, which was selected as a specific acute severity feature. From a clinical point of view, these patients showed higher liver function indices, as well as increased CRP and inflammatory markers, such as IL-6. Moreover, compared to control subjects, they present autoimmune disorders more frequently. Finally, their decreased diffusion lung capacity for carbon monoxide after six months of COVID-19 suggests that RTEL1 variants can contribute to the development of SARS-CoV-2-elicited lung fibrosis. Conclusion: RTEL1 ultra-rare variants can be considered as a predictive marker of COVID-19 severity, as well as a marker of pathological evolution in pulmonary fibrosis in the post-COVID phase. This notion can be used for a rapid screening in hospitalized infected people, for vaccine prioritization, and appropriate follow-up assessment for subjects at risk. Trial Registration NCT04549831 (www.clinicaltrial.org

An explainable model of host genetic interactions linked to COVID-19 severity

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, supporting their link with COVID-19 severity outcome

A genome-wide association study for survival from a multi-centre European study identified variants associated with COVID-19 risk of death

The clinical manifestations of SARS-CoV-2 infection vary widely among patients, from asymptomatic to life-threatening. Host genetics is one of the factors that contributes to this variability as previously reported by the COVID-19 Host Genetics Initiative (HGI), which identified sixteen loci associated with COVID-19 severity. Herein, we investigated the genetic determinants of COVID-19 mortality, by performing a case-only genome-wide survival analysis, 60 days after infection, of 3904 COVID-19 patients from the GEN-COVID and other European series (EGAS00001005304 study of the COVID-19 HGI). Using imputed genotype data, we carried out a survival analysis using the Cox model adjusted for age, age2, sex, series, time of infection, and the first ten principal components. We observed a genome-wide significant (P-value < 5.0 × 10−8) association of the rs117011822 variant, on chromosome 11, of rs7208524 on chromosome 17, approaching the genome-wide threshold (P-value = 5.19 × 10−8). A total of 113 variants were associated with survival at P-value < 1.0 × 10−5 and most of them regulated the expression of genes involved in immune response (e.g., CD300 and KLR genes), or in lung repair and function (e.g., FGF19 and CDH13). Overall, our results suggest that germline variants may modulate COVID-19 risk of death, possibly through the regulation of gene expression in immune response and lung function pathways