ABSTRACT
The coronavirus disease-19 (COVID-19) infection may remain asymptomatic or may have several different presentations. Although this disease primarily affects the respiratory system, systemic manifestations affecting the gastrointestinal, cardiovascular, neurological, otorhinolaryngologic, and ophthalmic systems have been reported. Ophthalmic signs may be the first and only sign of COVID-19 infection in children. In the current narrative review, we report the ophthalmic manifestations of COVID-19 in the pediatric age cohort. We performed a comprehensive literature search for the publications on ophthalmic manifestations of COVID-19 in children between 1 March 2020 and 1 January 2022 and compiled the ophthalmic manifestations of this entity among the pediatric population. Conjunctivitis is the most common ophthalmic manifestation in children and can develop at any stage of the disease. Ophthalmic manifestations are seen more commonly in children with severe systemic disease. Long-term and indirect consequence of the COVID-19 disease is the rise of myopia among children. Ophthalmic signs may be the first and only sign of COVID-19 infection in children. Pediatricians, as well as ophthalmologists, must keep observing all children with COVID-19 closely for ophthalmic signs.
ABSTRACT
Purpose: COVID-19-associated mucormycosis (CAM) was a serious public health problem during the second wave of COVID-19 in India. We planned to analyze public perceptions by sentiment analysis of Twitter data regarding CAM. Methods: In this observational study, the application programming interface (API) provided by the Twitter platform was used for extracting real-time conversations by using keywords related to mucormycosis (colloquially known as "black fungus"), from May 3 to August 29, 2021. Lexicon-based sentiment analysis of the tweets was done using the Vader sentiment analysis tool. To identify the overall sentiment of a user on any given topic, an algorithm to label a user "k" based on their sentiments was used. Results: A total of 4,01,037 tweets were collected between May 3 and August 29, 2021, and the peak frequency of 1,60,000 tweets was observed from May 17 to May 23, 2021. Positive sentiment tweets constituted a larger share as compared to negative sentiment tweets, with weekly variations. A temporal analysis of the demand for utilities showed that the demand was high in the initial period but decreased with time, which was associated with the availability of resources. Conclusion: Sentiment analysis using Twitter data revealed that social media platforms are gaining popularity to express one's emotions during the ongoing COVID-19 pandemic. In our study, time-based assessment of tweets showed a reduction over time in the frequency of negative sentiment tweets. The polarization in the retweet network of users, based on sentiment polarity, showed that the users were well connected, highlighting the fact that such issues bond our society rather than segregating it.