ABSTRACT
Background: XBB.1.5 is a new subvariant of the SARS-CoV-2 Omicron variant with increased transmissibility and immune escape potential. Twitter has been used to share information and assess this subvariant. Objectives: This study aims to investigate the channel graph, key influencers, top sources, most trends, and pattern discussion, as well as sentiment measures related to Covid-19 XBB.1.5 variant, by using social network analysis (SNA). Methods: This experiment involved the collection of Twitter data through the keywords, "XBB.1.5â³, and NodeXL, with the obtained information subsequently cleaned to remove duplication and irrelevant tweets. SNA was also performed by using analytical metrics to identify influential users and understand the patterns of connectivity among those discussing XBB.1.5. on Twitter. Moreover, the results were visualized through Gephi software, with sentiment analysis performed by using Azure Machine Learning to categorize tweets into three categories, namely positive, negative, and neutral. Results: A total of 43,394 XBB.1.5-based tweets were identified, with five key users observed with the highest betweenness centrality score (BCS), namely "ojimakohei"(red), mikito_777 (blue), "nagunagumomo" (green), "erictopol" (orange), w2skwn3 (yellow). The other hand, the in-degree, out-degree, betweenness, closeness, and eigenvector centrality scores of the top 10 Twitter users to explain various patterns and trends and "ojimakohei" was highly central in the network. Most of the top domains (sources) used in XBB.1.5 discourse originated from Twitter, Japanese websites (co.jp and or.jp), and scientific analysis links (biorxiv.org and cdc.gov). This analysis indicated that most of the tweets (61.35 %) were positively classified, accompanied by neutral (22.44 %) and negative (16.20 %) sentiments. Conclusion: Japan was actively engaged in evaluating the XBB.1.5 variant, with influential users playing a crucial role. The preference for sharing verified sources and the positive sentiment demonstrated a commitment to health awareness. We recommend fostering collaborations between health organizations, the government, and Twitter influencers to address COVID-19-related misinformation and its variants.
ABSTRACT
Drug-resistant tuberculosis (TB), which results mainly from the selection of naturally resistant strains of Mycobacterium tuberculosis (MTB) due to mismanaged treatment, poses a severe challenge to the global control of TB. Therefore, screening novel and unique drug targets against this pathogen is urgently needed. The metabolic pathways of Homo sapiens and MTB were compared using the Kyoto Encyclopedia of Genes and Genomes tool, and further, the proteins that are involved in the metabolic pathways of MTB were subtracted and proceeded to protein-protein interaction network analysis, subcellular localization, drug ability testing, and gene ontology. The study aims to identify enzymes for the unique pathways for further screening to determine the feasibility of the therapeutic targets. The qualitative characteristics of 28 proteins identified as drug target candidates were studied. The results showed that 12 were cytoplasmic, 2 were extracellular, 12 were transmembrane, and 3 were unknown. Furthermore, druggability analysis revealed 14 druggable proteins, of which 12 were novel and responsible for MTB peptidoglycan and lysine biosynthesis. The novel targets obtained in this study are used to develop antimicrobial treatments against pathogenic bacteria. Future studies should further shed light on the clinical implementation to identify antimicrobial therapies against MTB.
