A COVID-19 news covers subtopics like infections, deaths, the economy, jobs, and more. The proposed method generates a news summary based on the subtopics of a reader's interest. It extracts a centroid having the lexical pattern of the sentences on those subtopics by the frequently used words in them. The centroid is then used as a query in the vector space model (VSM) for sentence classification and extraction, producing a query focused summarization (QFS) of the documents. Three approaches, TF-IDF, word vector averaging, and auto-encoder are experimented to generate sentence embedding that are used in VSM. These embeddings are ranked depending on their similarities with the query embedding. A Novel approach has been introduced to find the value for the similarity parameter using a supervised technique to classify the sentences. Finally, the performance of the method has been assessed in two different ways. All the sentences of the dataset are considered together in the first assessment and in the second, each document wise group of sentences is considered separately using fivefold cross-validation. The proposed method has achieved a minimum of 0.60 to a maximum of 0.63 mean F1 scores with the three sentence encoding approaches on the test dataset.

The COVID-19 pandemic has resulted in a surge of fake news, creating public health risks. However, developing an effective way to detect such news is challenging, especially when published news involves mixing true and false information. Detecting COVID-19 fake news has become a critical task in the field of natural language processing (NLP). This paper explores the effectiveness of several machine learning algorithms and fine-tuning pre-trained transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT) and COVID-Twitter-BERT (CT-BERT), for COVID-19 fake news detection. We evaluate the performance of different downstream neural network structures, such as CNN and BiGRU layers, added on top of BERT and CT-BERT with frozen or unfrozen parameters. Our experiments on a real-world COVID-19 fake news dataset demonstrate that incorporating BiGRU on top of the CT-BERT model achieves outstanding performance, with a state-of-the-art F1 score of 98%. These results have significant implications for mitigating the spread of COVID-19 misinformation and highlight the potential of advanced machine learning models for fake news detection.

After a significant hiatus imposed by the COVID-19 pandemic, we hereby restart Xenotransplantation literature updates. With the recently performed clinical xenotransplantation cases and the much-heightened interest in the field, we have determined that this is an optimal time to reinstate this section. There has been an invigorated focus on unique challenges posed by pig-to-human xenotransplantation, and specific attention will be given to this aspect. In this issue, we aimed to cover the gap and compiled the most relevant publications from March 2021 to March 2023.

Researchers aim to make monoclonal antibodies that mutations in SARS-CoV-2 won't thwart.

Consensus grows for abandoning the ancestral strain to improve immune responses.

Since vaccines against COVID-19 are available, it has been debated if immunosuppressed patients with autoimmune blistering diseases (AIBDs) should be advised to interrupt the immunosuppressive therapy before receiving the vaccine, with consequent risk to experience a flare of disease. In the present study, we measured the neutralizing antibodies production after anti-SARS-CoV-2 vaccination in patients with AIBDs on immunosuppressive treatment, compared to healthy controls. Our results give strength to the hypothesis that these patients do not need to discontinue their therapy to produce effective levels of neutralizing antibodies, in other words to achieve successful protection.

Societies often respond to a crisis by attributing blame to some groups while constructing others as victims and heroes. While it has received scant sociological attention, 'panic buying' is a critical indicator of such public sentiment at the onset of a crisis, and thus a crucial site for analysis. This article traces dynamics of blame in news media representations of an extreme period of panic buying during COVID-19 in Australia. Analysis reveals that lower socio-economic and ethnically diverse consumers were blamed disproportionately. Unlike wealthier consumers who bulk-bought online, shoppers filling trollies in-store were depicted as selfish and shameful, described using dehumanising language, and portrayed as 'villains' who threatened social order. Supermarkets were cast simultaneously as 'victims' of consumer aggression and 'heroes' for their moral leadership, trustworthiness and problem-solving. This portrayal misunderstands the socio-emotional drivers of panic buying, exacerbates stigma towards already disadvantaged groups, and veils the corporate profiteering that encourages stockpiling.

Moves by WHO and U.S. dial back data collection, reporting mandates.

In order to take advantage of the power of social media to promote vaccination, this study reveals the mechanisms of positive and negative impacts of social media news on vaccine hesitancy and vaccination behavior. Based on the stimulus-organism-response (S-O-R) framework, we developed a research model to understand the effects of vaccine safety news and risk news from social media (external stimuli) on individuals' psychological organism (i.e., safety perception and risk perception) and consequent behavioral response, vaccine hesitancy and vaccination behavior. The proposed model was tested by partial least square structural equation modeling (PLS-SEM) on a sample gathered in China from September 2021 to November 2021 and from February 2022 to April 2022 (valid responses = 1579). The results found that the relationship between vaccine risk news from social media and risk perception was higher than the relationship between vaccine safety news from social media and safety perception. Individuals are more sensitive to vaccine risk news than safety news on social media. Moreover, both safety perception and risk perception explained the critical psychological mechanisms behind vaccine hesitancy. Interestingly, ego network density mitigated the effect of safety news on safety perception and the effect of risk news on risk perception. The findings contribute to the S-O-R model, the research on social media effects, and the literature on vaccination attitudes and behaviors. This study also informs public health officials about leveraging the power of social media to motivate the public to accept the COVID-19 vaccines.