Depression in adolescence is influenced by the environment, family members, social relationships, and genetic factors. Gene polymorphisms of serotonin receptors were associated with mental/psychiatric disorders, including impulsive, aggressive, violent, antisocial or criminal conduct, schizophrenia, eating disorders, alexithymia, autism, and major depressive disorder (MDD). Few studies reported the association between serotonin receptor polymorphisms and depressive symptoms in nonclinical subjects. The present study aimed to evaluate the serotonin receptor polymorphisms in nonclinical adolescents presenting depressive symptoms. The results clearly demonstrated that nonclinical adolescents with severe depressive symptoms had a high concentration of GA + AA in the recessive model and of AA in the homozygous model of the rs6311 polymorphism. The data indicated that the A allele was associated with severe depressive symptoms. Moreover, the analysis highlighted a trend of association of TT + CT in the recessive model for rs6313 polymorphism and severe symptoms. In conclusion, our study confirms that the A allele for rs6311 represents a risk factor, and the allele T for rs6313 could be a possible risk factor for severe depressive symptoms. As a consequence, the allele G for rs6311 and the allele C for rs6313 could be protective against severe depressive symptoms. Therefore, it might be appropriate to work preventively on adolescents with the A allele for rs6311 and T allele for rs6313 polymorphism to reduce the possibility of developing depressive symptoms and to preserve mental health in young people.

In Europe, more than two-thirds of the population live in urban areas. The management of urban green areas and trees is becoming increasingly relevant involving different players and stakeholders, as well for keeping a focus on the Sustainable Development Goals. Society and policy makers are often unaware of the disservices that neglecting these areas can cause. Appropriate monitoring interventions can protect both citizens and trees themselves. The aim of the study was to assess the risk potential in urban trees in Bologna suburbs (Italy). For each tree in the city of Bologna, three target variables identifying the number and categories of streets, buildings, and walking and cycle paths near the tree were considered and used as covariates. A multiple regression model assessing the relationship between a dependent synthetic spatial variable (digital number, DN), proxy of the likelihood of tree failure, and the three independent covariates was estimated. Both the number of streets and walking and cycle paths in the area surrounding a tree are shown to be significantly associated with the DN value. The use of open data can assist in monitoring and maintaining urban green areas. The tool supports a virtuous circle between stakeholders in urban systems through sustainability and efficiency.