Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence.

BACKGROUND: The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic. OBJECTIVE: The aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19. METHODS: This study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19-related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted. RESULTS: There were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19-related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment. CONCLUSIONS: This study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public's response to COVID-19 and help officials navigate the pandemic.

Student engagement and comprehension using tactile and virtual learning.

PURPOSE/OBJECTIVES: Dental students' levels of engagement and comprehension were assessed using tactile learning (with physical teeth) versus virtual learning (using computer images) in a dental histology course. Differences in engagement and comprehension by learning preferences were also examined. METHODS: One hundred first-year dental students were randomly divided into twenty teams of five students. Conditions were counterbalanced such that half the students were given physical tooth models (Group A) while the other half were given access to virtual images of tooth models (Group B) during the first session. Conditions were switched for a second session. Both groups completed the same learning exercise and formative assessment (quiz) after each session. A survey assessed students' learning preferences and attitudes. RESULTS: There were no differences by group assignment in both formative assessment scores and learning preferences (p > 0.10). For all students, comprehension of material was significantly higher in the tactile condition (with physical teeth) compared to the virtual experience (p = 0.01), but there was no difference in engagement between conditions (p = 0.35). Students who preferred learning with physical tooth models had higher engagement when using the physical tooth models compared to those who preferred virtual tooth models (p = 0.04), and significantly more positive attitudes toward physical tooth models (p's < 0.0001). CONCLUSION: Results suggest that for dental students, learning activities involving handling physical teeth may produce greater comprehension than viewing virtual images of teeth on a screen. More research is needed to determine the circumstances when virtual images can be substituted for physical models or be a beneficial adjunct method.

Altered bacterial metabolism, not coenzyme Q content, is responsible for the lifespan extension in Caenorhabditis elegans fed an Escherichia coli diet lacking coenzyme Q.

Coenzyme Q(n) is a fully substituted benzoquinone containing a polyisoprene tail of distinct numbers (n) of isoprene groups. Caenorhabditis elegans fed Escherichia coli devoid of Q(8) have a significant lifespan extension when compared to C. elegans fed a standard 'Q-replete'E. coli diet. Here we examine possible mechanisms for the lifespan extension caused by the Q-less E. coli diet. A bioassay for Q uptake shows that a water-soluble formulation of Q(10) is effectively taken up by both clk-1 mutant and wild-type nematodes, but does not reverse lifespan extension mediated by the Q-less E. coli diet, indicating that lifespan extension is not due to the absence of dietary Q per se. The enhanced longevity mediated by the Q-less E. coli diet cannot be attributed to dietary restriction, different Qn isoforms, reduced pathogenesis or slowed growth of the Q-less E. coli, and in fact requires E. coli viability. Q-less E. coli have defects in respiratory metabolism. C. elegans fed Q-replete E. coli mutants with similarly impaired respiratory metabolism due to defects in complex V also show a pronounced lifespan extension, although not as dramatic as those fed the respiratory deficient Q-less E. coli diet. The data suggest that feeding respiratory incompetent E. coli, whether Q-less or Q-replete, produces a robust life extension in wild-type C. elegans. We believe that the fermentation-based metabolism of the E. coli diet is an important parameter of C. elegans longevity.