Comparative genomic and transcriptomic analyses of African swine fever virus strains.

African swine fever (ASF) is the most devastating disease caused by the African swine fever virus (ASFV), impacting the pig industry worldwide and threatening food security and biodiversity. Although two vaccines have been approved in Vietnam to combat ASFV, the complexity of the virus, with its numerous open reading frames (ORFs), necessitates a more diverse vaccine strategy. Therefore, we focused on identifying and investigating the potential vaccine targets for developing a broad-spectrum defense against the virus. This study collected the genomic and/or transcriptomic data of different ASFV strains, specifically from in vitro studies, focusing on comparisons between genotypes I, II, and X, from the National Center for Biotechnology Information (NCBI) database. The comprehensive analysis of the genomic and transcriptomic differences between high- and low-virulence strains revealed six early genes, 13 late genes, and six short genes as potentially essential ORFs associated with high-virulence. In addition, many other ORFs (e.g., 14 multigene family members) are worth investigating. The results of this study provided candidate ORFs for developing ASF vaccines and therapies.

Development and preparation of active starch films carrying tea polyphenol.

Starch films incorporated with tea polyphenol (TP) were developed to produce active food packaging. The effect of the incorporation of TP with different content on the structure, physicochemical properties, antioxidant activity and antimicrobial activity of the starch films was systematically evaluated. Results showed that TP was well dispersed in the starch matrix, which induced a slight influence on the surface and barrier properties of the films. TP addition led to an important improvement in antioxidant capability, as well as inhibition efficiency against the microorganisms of S. aureus and E. coli. However, a decrease in mechanical properties of films was observed. Moreover, a new automatic counting method which combined the computer vision and machine learning algorithm was developed to identify and count the colonies, and the method performed much faster without subjective uncertainty.