The exponential growth of social media has brought with it an increasing propagation of hate speech and hate based propaganda. Hate speech is commonly defined as any communication that disparages a person or a group on the basis of some characteristics such as race, colour, ethnicity, gender, sexual orientation, nationality, religion. Online hate diffusion has now developed into a serious problem and this has led to a number of international initiatives being proposed, aimed at qualifying the problem and developing effective counter-measures. The aim of this paper is to analyse the knowledge structure of hate speech literature and the evolution of related topics. We apply co-word analysis methods to identify different topics treated in the field. The analysed database was downloaded from Scopus, focusing on a number of publications during the last thirty years. Topic and network analyses of literature showed that the main research topics can be divided into three areas: “general debate hate speech versus freedom of expression”,“hate-speech automatic detection and classification by machine-learning strategies”, and “gendered hate speech and cyberbullying”. The understanding of how research fronts interact led to stress the relevance of machine learning approaches to correctly assess hatred forms of online speech.
Osteoporosis is an age-related bone disease, affecting mainly postmenopausal women, characterized by decreased bone mineral density (BMD) and consequent risk of fractures. Homocysteine (Hcy), a sulfur-aminoacid whose serum level is regulated by methylenetrahydrofolate reductase (MTHFR) activity and vitamin B12 and folate as cofactors, is a risk factor for inflammatory diseases. Literature data concerning the link between Hcy and osteoporosis are still debated. The aim of our study was to assess the relationship among Hcy and BMD, inflammation, vitamin status and bone turnover in postmenopausal osteoporosis. In 252 postmenopausal women, BMD was measured by dual-energy X-ray absorptiometry (DXA). In addition to serum Hcy, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) and bone turnover markers (bone alkaline phosphatase-BAP, osteocalcin-OC, C-terminal telopeptide of type I collagen (CTX), vitamin deficiencies and MTHFR-C677T polymorphism were evaluated. Hcy, inflammation, bone resorption markers and prevalence of C677T polymorphism were higher, whereas vitamin D, B12, folate, and bone formation markers were lower in women with decreased BMD compared to those with normal BMD. Our results suggest a significant association between Hcy, BMD and inflammation in postmenopausal osteoporosis. The regulation of Hcy overproduction and the modulation of the inflammatory substrate could represent additional therapeutic approaches for osteoporosis prevention.
Introduction: It is essential to consider the clinical assessment of psychological aspects in patients with Diabetes Mellitus (DM), in order to prevent potentially adverse self-management care behaviors leading to diabetes-related complications, including declining levels of Quality of Life (QoL) and negative metabolic control.Purpose: In the framework of Structural Equation Modeling (SEM), the specific aim of this study is to evaluate the influence of distressed personality factors as Negative Affectivity (NA) and Social Inhibition (SI) on diabetes-related clinical variables (i.e., QoL and glycemic control).Methods: The total sample consists of a clinical sample, including 159 outpatients with Type 2 Diabetes Mellitus (T2DM), and a control group composed of 102 healthy respondents. All participants completed the following self- rating scales: The Type D Scale (DS14) and the World Health Organization QoL Scale (WHOQOLBREF). Furthermore, the participants of the clinical group were assessed for HbA1c, disease duration, and BMI. The observed covariates were BMI, gender, and disease duration, while HbA1c was considered an observed variable.Results: SEM analysis revealed significant differences between groups in regards to the latent construct of NA and the Environmental dimension of QoL. For the clinical sample, SEM showed that NA had a negative impact on both QoL dimensions and metabolic control.Conclusions: Clinical interventions aiming to improve medication adherence in patients with T2DM should include the psychological evaluation of Type D Personality traits, by focusing especially on its component of NA as a significant risk factor leading to negative health outcomes.
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