Adenoviral and mRNA vaccines encoding the viral spike (S) protein have been deployed globally to contain severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Older individuals are particularly vulnerable to severe infection, probably reflecting age-related changes in the immune system, which can also compromise vaccine efficacy. It is nonetheless unclear to what extent different vaccine platforms are impacted by immunosenescence. Here, we evaluated S protein-specific immune responses elicited by vaccination with two doses of BNT162b2 or ChAdOx1-S and subsequently boosted with a single dose of BNT162b2 or mRNA-1273, comparing age-stratified participants with no evidence of previous infection with SARS-CoV-2. We found that aging profoundly compromised S protein-specific IgG titers and further limited S protein-specific CD4⁺ and CD8⁺ T cell immunity as a probable function of progressive erosion of the naive lymphocyte pool in individuals vaccinated initially with BNT162b2. Our results demonstrate that primary vaccination with ChAdOx1-S and subsequent boosting with BNT162b2 or mRNA-1273 promotes sustained immunological memory in older adults and potentially confers optimal protection against coronavirus disease 2019.

Amongst the several consequences of the COVID-19 pandemic, we should include psychological effects on the population. The mental health consequences of lockdown are affected by several factors. The most important are: the duration of the social isolation period, the characteristics of the living space, the number of online (virtual) and offline (physical) contacts and perceived contacts’ closeness, individual characteristics, and the spread of infection in the geographical area of residence. In this paper, we investigate the possible effects of environmental, social and individual characteristics (predictors) on mental health (response) during the COVID-19 lockdown period. The relationship between mental health and predictors can be studied with a multivariate linear regression model, because “mental health” is a multidimensional concept. This work provides a contribution to the debate about the factors affecting mental health in the period of the COVID-19 lockdown, with the application of an innovative approach based on a multivariate regression analysis and a combined permutation test on data collected in a survey conducted in Italy in 2020.

Research into how environmental management practices impact firm performance has largely overlooked the service sector's small and medium‐sized enterprises (SMEs). Drawing on industry, resource, and institution‐based strategy perspectives, this study investigates how adopting resource efficiency (RE) practices within the framework of a circular economy (CE) supports European service SMEs in achieving carbon neutrality (CN) and reducing production costs (PCs). The study also examines difficulties in adapting to environmental regulations among adopters, their associated impact on performance, and the importance of having a CN strategy. A combined permutation test conducted on 681 European service SMEs from 35 countries revealed––after controlling for sectoral differences––that SMEs without RE practices within the CE framework faced higher PCs, a critical factor for firm performance. Additionally, transportation and storage SMEs with a CN strategy demonstrated greater adoption of RE practices. Implications for theory, practice, and future research are discussed.

In this work, a nonparametric method is proposed to jointly test the significance of the regression coefficient estimates in a logistic regression model and identify which explanatory variables are effective in predicting the binary response. The motivating example is related to the factors affecting the propensity of Italian Small Medium Enterprises (SMEs) to innovate. The explanatory variables of the model represent firms’ characteristics, such as size and age, and the possible effect of the sector of economic activity is taken into account by including a set of binary variables as control factors. The dependent variable indicates whether a company, in the period under study, introduced at least one product or process innovation. Therefore, it is also dichotomous, and the logistic regression model is appropriate for representing the relationship between explanatory variables and dependent variable. Specifically, the logit transformation of the firm’s propensity to innovate, i.e., the probability that a company randomly chosen from the population of Italian SMEs has introduced an innovation or, equivalently, the proportion of innovative companies among the Italian SMEs, is expressed as a linear function of the predictors (explanatory and control variables). The proposed test is based on the permutation approach and satisfies important statistical properties, proved in a simulation study. The test is more flexible and robust than the classic parametric approach, and is preferable to typical stepwise regression procedures for the selection of a parsimonious and effective model.