Using text mining and sentiment analysis to analyse YouTube Italian videos concerning vaccination.

BACKGROUND: Recently, social networks have become a popular source of information on health topics. Particularly, in Italy, there is a lively discussion on the web regarding vaccines also because there is low vaccination coverage, vaccines hesitancy, and anti-vaccine movements. For these reasons, in 2017, Institutions have introduced a law to force children to make ten compulsory vaccines for school attendance and proposed a vaccination campaign. On social networks, this law has fostered a fierce discussion between pro-vaccinations and anti-vaccinations people. This paper aims to understand if and how the population's opinion has changed before the law and after the vaccination campaign using the titles of the videos uploaded on Youtube in these periods. METHOD: Using co-occurrence network (CON) and sentiment analysis, we analysed the topics of YouTube Italian videos on vaccines in 2017 and 2018. RESULTS: The CON confirms that vaccinations were very disapproved before the law. Instead, after the communication campaign, people start to be less critical. The sentiment analysis shows that the intense vaccination campaign also promoted by medical doctors pushed the sentiment to change polarity from a prevailing negative opinion in 2017 (52% negative) to a positive one in 2018 (54% positive). CONCLUSION: At the population level, the potential misinformation of social networks could be significant and is a real risk for health. Our study highlights that vaccination campaigns on social networks could be an essential instrument of health policies and a sharp weapon to fight ignorance and misrepresentations of non-qualified people influencing individuals' decision-making.

A survey on the role of artificial intelligence in managing Long COVID.

In the last years, several techniques of artificial intelligence have been applied to data from COVID-19. In addition to the symptoms related to COVID-19, many individuals with SARS-CoV-2 infection have described various long-lasting symptoms, now termed Long COVID. In this context, artificial intelligence techniques have been utilized to analyze data from Long COVID patients in order to assist doctors and alleviate the considerable strain on care and rehabilitation facilities. In this paper, we explore the impact of the machine learning methodologies that have been applied to analyze the many aspects of Long COVID syndrome, from clinical presentation through diagnosis. We also include the text mining techniques used to extract insights and trends from large amounts of text data related to Long COVID. Finally, we critically compare the various approaches and outline the work that has to be done to create a robust artificial intelligence approach for efficient diagnosis and treatment of Long COVID.

A fuzzy approach for analysing equitable and sustainable well-being in Italian regions.

OBJECTIVES: Recently, the Italian Institute of Statistics (ISTAT) and the National Council for Economy and Labor (CNEL) have proposed a measure for the equitable and sustainable well-being called the BES ("Benessere Equo e Sostenibile"). This paper aims to propose an original application of the fuzzy k-means approach to providing an analysis of the Italian regions according to their BES. METHODS: The fuzzy k-means algorithm was used for clustering the Italian regions according to BES data 2015. Afterwards, a principal component analysis was conducted to show and interpret the results. RESULTS: There is a clear difference between the regions of the North and the South. The only exceptions are represented by Lazio and Abruzzo, which belong to both groups with almost equal degrees of truth. Moreover, Trentino-Alto Adige and Valle d'Aosta exhibit the best condition, whilst Molise is the worst region. CONCLUSIONS: This study reveals that some Italian regions are in a state of backwardness regarding health, environment, minimum economic conditions, subjective well-being, education, employment conditions, social relationships, and working conditions. Therefore, institutions should consider local policies to address these issues.

Significance of persistence of antibodies against Leishmania infantum in Sicilian patients affected by acute visceral leishmaniasis.

The background of this article is as follows: Few data are available about the persistence of serum-specific IgG antibodies to L. infantum after acute VL. The objective of this article is to evaluate the persistence of antibodies against L. infantum in patients healed from acute VL, and the kinetic of the same antibodies observed in 2 cases of VL relapse and 2 cases of resistance to therapy. The methods which we used to obtain our objective are the following: 55 apparently immunocompetent, HIV-negative patients were examined for antibodies to L. infantum by IFAT over 14 years period, and we got the following results: Serum-specific IgG antibodies titers decrease slowly, but constantly. In the patients with a diagnosis of VL relapse, the kinetic of antibodies was characterized by an initial reduction, and a subsequent antibody levels rapidly increase, while in the patients with a clinical and parasitological diagnosis of VL not responding to specific therapy, we demonstrated persistent high level of antibodies to L. infantum. Finally, we conclude that specific antibodies to L. infantum might persist for many years, and decrease slowly, but steadily. The persistence of these specific antibodies is not related to poor therapeutic response or prognosis, but an acute increase in their levels might be a sentinel of a VL relapse, while persistence of high antibody levels could suggest a resistance to therapy.