ABSTRACT
Cross-contamination between patient and dentist is a real threat that has not been adequately studied. Staphylococcus aureus, through its characteristic genetic plasticity, has managed to develop multiple virulence and antibiotic resistance proteins. The antibiotic susceptibility profile and the presence of the blaZ and mecA genes that encode resistance to penicillin and methicillin, respectively, were analyzed in strains isolated from multipurpose boxes used by dental students at the Catholic University of Cuenca. These boxes are used to transport instruments and material. From the universe of study (249 boxes) 139 samples were obtained from boxes of the students who accepted and signed a consent to participate. Eight strains of S. aureus were identified, of which, through antibiogram analysis, it was found that seven were resistant to penicillin and two strains resistant to cefoxitin (MRSA strains). In molecular analysis, the mecA gene was identified in two strains, while the blaZ gene was found in all of them. It was concluded that the rate of S. aureus found in this study was low due to various factors, possibly including increased vigilance and cleanliness due to the COVID-19 pandemic during the study. © FUNPEC-RP www.funpecrp.com.br.
ABSTRACT
This document intends to analyze the sentiments underlying COVID-19 vaccination tweets. To achieve the objective, 38,034 publications from this social network are extracted through data mining, applying Machine Learning techniques, specifically sentiment analysis and network analysis, to identify the feelings expressed by Twitter users. We also identify the most relevant Twitter accounts on vaccination issues. The results suggest that feelings about vaccines are primarily negative;fear and anger, respectively, are the most recurring emotions in Twitter opinions. Moreover, we noted that the most relevant accounts belong to the media, politicians, and influencers, classified according to their feelings toward the vaccine. Opposition to the government with feelings of anger and opposition to recognized media with joyful emotions stand out. © 2022 Universidad de La Sabana. All rights reserved.
ABSTRACT
To reduce the rate of contagion by Covid-19, the Colombian government has adopted, among other measures, for mandatory isolation, with divided opinions, because despite helping to reduce the spread of the virus, it generates mental and economic problems that are difficult to overcome. The objective of this document was to analyze the underlying sentiments in the Twitter comments related to isolation, identifying the topics and words most frequently used in this context. A machine learning algorithm was built to identify sentiments in 72,564 posts and a social network analysis was applied establishing the most frequent topics in the data sets. The results suggest that the algorithm is highly accurate in classifying feelings. Also, as the isolation extends, comments related to the quarantine grow proportionally. Fear was identified as the predominant feeling throughout the period of confinement in Colombia.
ABSTRACT
The effects of the different message strategies related to COVID-19 on the generation of eWOM were analyzed;that is, if the publications referring to the pandemic receive greater participation by users of social networks in Colombia. 562 company posts on Facebook were reviewed, of which 382 were subjected to the negative binomial regression model. It was found that no message strategy related to COVID-19 affects the rate of comments. The influence of different types of content on reactions and shared content was also identified. It is concluded that social networks are recreation and entertainment scenarios;therefore, the informative content does not generate impacts on the volume of comments, reactions, or share content.