Edinburgh Napier University

About: Edinburgh Napier University is a education organization based out in Edinburgh, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2665 authors who have published 6859 publications receiving 175272 citations.

Comparative toxicity of standard nickel and ultrafine nickel in lung after intratracheal instillation.

Abstract: A comparison was made of the bronchoalveolar lavage fluid (BALF) response to ultrafine nickel (Uf-Ni) and standard-sized nickel (Std-Ni). Rats were intratracheally instilled with 0, 0.1, 0.5, 1 and 5 mg Uf-Ni and Std-Ni, respectively. At 3 d after instillation, the body weight and wet lung weight were determined. At the same time, BALF was analyzed for lactate dehydrogenase (LDH), total protein (TP), tumor necrosis factor-alpha (TNF-alpha), and total cell and differential cell counts. The results showed that indicators of lung injury and inflammation in BALF were markedly raised with increased Uf-Ni and Std-Ni for each from 0 to 1 mg, and there were no differences in the indices between instillation of Uf-Ni at 1 mg and 5 mg. The results also showed that the effects of Uf-Ni on the indices were significantly higher than those of Std-Ni. Additional groups of rats were intratracheally instilled with 1 mg of Uf-Ni or Std-Ni, and wet lung weight and BALF profiles were analyzed at 1, 3, 7, 15 and 30 d later. The effect of Uf-Ni and Std-Ni on indices that can be presumed to reflect epithelial injury and permeability (LDH or TP), and release of proinflammatory cytokine (TNF-alpha) were increased throughout the 30 d post-exposure and the effects of Uf-Ni on these indices were significantly higher than those of Std-Ni from 1 to 30 d after instillation. Moreover, the number of neutrophils and LDH activity in BALF of rats after exposure to Uf-Ni were significantly greater than those of Std-Ni-exposed rats up to 30 d after instillation. Our findings suggest that Uf-Ni has a much more toxic effect on the lung than St-Ni, but the mechanism remains to be elucidated.

Artificial Intelligence-Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study.

Abstract: Background: Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration of vaccines is required, which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiments toward vaccines, which can help guide educational campaigns and other targeted policy interventions. Objective: The aim of this study was to develop and apply an artificial intelligence–based approach to analyze public sentiments on social media in the United Kingdom and the United States toward COVID-19 vaccines to better understand the public attitude and concerns regarding COVID-19 vaccines. Methods: Over 300,000 social media posts related to COVID-19 vaccines were extracted, including 23,571 Facebook posts from the United Kingdom and 144,864 from the United States, along with 40,268 tweets from the United Kingdom and 98,385 from the United States from March 1 to November 22, 2020. We used natural language processing and deep learning–based techniques to predict average sentiments, sentiment trends, and topics of discussion. These factors were analyzed longitudinally and geospatially, and manual reading of randomly selected posts on points of interest helped identify underlying themes and validated insights from the analysis. Results: Overall averaged positive, negative, and neutral sentiments were at 58%, 22%, and 17% in the United Kingdom, compared to 56%, 24%, and 18% in the United States, respectively. Public optimism over vaccine development, effectiveness, and trials as well as concerns over their safety, economic viability, and corporation control were identified. We compared our findings to those of nationwide surveys in both countries and found them to correlate broadly. Conclusions: Artificial intelligence–enabled social media analysis should be considered for adoption by institutions and governments alongside surveys and other conventional methods of assessing public attitude. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccines, help address the concerns of vaccine sceptics, and help develop more effective policies and communication strategies to maximize uptake.