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There is no multi-country/multi-language study testing a-priori multivariable associations between non-modifiable/modifiable factors and validated wellbeing/multidimensional mental health outcomes before/during the COVID-19 pandemic. Moreover, studies during COVID-19 pandemic generally do not report on representative/weighted non-probability samples. The Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) is a multi-country/multi-language survey conducting multivariable/LASSO-regularized regression models and network analyses to identify modifiable/non-modifiable factors associated with wellbeing (WHO-5)/composite psychopathology (P-score) change. It enrolled general population-representative/weighted-non-probability samples (26/04/2020-19/06/2022). Participants included 121,066 adults (age=42±15.9 years, females=64 %, representative sample=29 %) WHO-5/P-score worsened (SMD=0.53/SMD=0.74), especially initially during the pandemic. We identified 15 modifiable/nine non-modifiable risk and 13 modifiable/three non-modifiable protective factors for WHO-5, 16 modifiable/11 non-modifiable risk and 10 modifiable/six non-modifiable protective factors for P-score. The 12 shared risk/protective factors with highest centrality (network-analysis) were, for non-modifiable factors, country income, ethnicity, age, gender, education, mental disorder history, COVID-19-related restrictions, urbanicity, physical disorder history, household room numbers and green space, and socioeconomic status. For modifiable factors, we identified medications, learning, internet, pet-ownership, working and religion as coping strategies, plus pre-pandemic levels of stress, fear, TV, social media or reading time, and COVID-19 information. In multivariable models, for WHO-5, additional non-modifiable factors with |B|>1 were income loss, COVID-19 deaths. For modifiable factors we identified pre-pandemic levels of social functioning, hobbies, frustration and loneliness, and social interactions as coping strategy. For P-scores, additional non-modifiable/modifiable factors were income loss, pre-pandemic infection fear, and social interactions as coping strategy. COH-FIT identified vulnerable sub-populations and actionable individual/environmental factors to protect well-being/mental health during crisis times. Results inform public health policies, and clinical practice., Competing Interests: Conflict of interest Conflict of interest statements of all authors are detailed in eTable 12., (Copyright © 2024. Published by Elsevier B.V.)

The outbreak of the pandemic associated with Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) led researchers to find new potential treatments, including nonpharmacological molecules such as zinc (Zn2+). Specifically, the use of Zn2+ as a therapy for SARS-CoV-2 infection is based on several findings: 1) the possible role of the anti-inflammatory activity of Zn2+ on the aberrant inflammatory response triggered by COronaVIrus Disease 19 (COVID-19), 2) properties of Zn2+ in modulating the competitive balance between the host and the invading pathogens, and 3) the antiviral activity of Zn2+ on a number of pathogens, including coronaviruses. Furthermore, Zn2+ has been found to play a central role in regulating brain functioning and many disorders have been associated with Zn2+ deficiency, including neurodegenerative diseases, psychiatric disorders, and brain injuries. Within this context, we carried out a narrative review to provide an overview of the evidence relating to the effects of Zn2+ on the immune and nervous systems, and the therapeutic use of such micronutrients in both neurological and infective disorders, with the final goal of elucidating the possible use of Zn2+ as a preventive or therapeutic intervention in COVID-19. Overall, the results from the available evidence showed that, owing to its neuroprotective properties, Zn2+ supplementation could be effective not only on COVID-19-related symptoms but also on virus replication, as well as on COVID-19-related inflammation and neurological damage. However, further clinical trials evaluating the efficacy of Zn2+ as a nonpharmacological treatment of COVID-19 are required to achieve an overall improvement in outcome and prognosis., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

The rare type match problem is an evaluative challenging situation in which the analysis of a DNA profile reveals the presence of (at least) one allele which is not contained in the reference database. This situation is challenging because an estimate for the frequency of occurrence of the profile in a given population needs sophisticated evaluative procedures. The rare type match problem is very common when the DIP-STR marker system, which has proven itself very useful for dealing with unbalanced DNA mixtures, is used, essentially due to the limited size of the available database. The object-oriented Bayesian network proposed in Cereda et al. [7] to assess the value of the evidence for general scenarios, was not designed to deal with this particular situation. In this paper, the model is extended and partially modified to be able to calculate the full Bayesian likelihood ratio in presence of any (observed and not yet observed) allele of a given profile. The method is based on the approach developed in Cereda [5] for Y-STR data. Alternative solutions, such as the plug-in approximation and an empirical Bayesian methodology are also proposed and compared with the results obtained with the full Bayesian approach., (Copyright © 2017. Published by Elsevier B.V.)

The genetic characterization of unbalanced mixed stains remains an important area where improvement is imperative. In fact, using the standard tools of forensic DNA profiling (i.e., STR markers), the profile of the minor contributor in mixed DNA stains cannot be successfully detected if its quantitative share of DNA is less than 10% of the mixed trace. This is due to the fact that the major contributor's profile "masks" that of the minor contributor. Besides known remedies to this problem, such as Y-STR analysis, a new compound genetic marker that consists of a Deletion/Insertion Polymorphism (DIP) linked to a Short Tandem Repeat (STR) polymorphism, has recently been developed and proposed. These novel markers are called DIP-STR markers. This paper compares, from a statistical and forensic perspective, the potential usefulness of these novel DIP-STR markers (i) with traditional STR markers in cases of moderately unbalanced mixtures, and (ii) with Y-STR markers in cases of female-male mixtures. This is done through a comparison of the distribution of 100,000 likelihood ratio values obtained using each method on simulated mixtures. This procedure is performed assuming, in turn, the prosecution's and the defence's point of view., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

The genetic characterization of unbalanced mixed stains remains an important area where improvement is imperative. In fact, with current methods for DNA analysis (Polymerase Chain Reaction with the SGM Plus multiplex kit), it is generally not possible to obtain a conventional autosomal DNA profile of the minor contributor if the ratio between the two contributors in a mixture is smaller than 1:10. This is a consequence of the fact that the major contributor's profile 'masks' that of the minor contributor. Besides known remedies to this problem, such as Y-STR analysis, a new compound genetic marker that consists of a Deletion/Insertion Polymorphism (DIP), linked to a Short Tandem Repeat (STR) polymorphism, has recently been developed and proposed elsewhere in literature. The present paper reports on the derivation of an approach for the probabilistic evaluation of DIP-STR profiling results obtained from unbalanced DNA mixtures. The procedure is based on object-oriented Bayesian networks (OOBNs) and uses the likelihood ratio as an expression of the probative value. OOBNs are retained in this paper because they allow one to provide a clear description of the genotypic configuration observed for the mixed stain as well as for the various potential contributors (e.g., victim and suspect). These models also allow one to depict the assumed relevance relationships and perform the necessary probabilistic computations., (Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.)