No abstract

The purpose of the present study was to evaluate the use of cationic niosomes composed of Span20:cholesterol:cationic lipid (N ,N-dimyristeroyloxyethyl-spermine) at the molar ratio of 2.5:2.5:0.5 mM combined with hollow microneedle (MN) devices for in vivo skin immunization of plasmid DNA-encoding ovalbumin (pOVA). The results revealed that using hollow MNs with cationic niosomes for pOVA penetration successfully induced both humoral and cell-mediated immune responses including immunoglobulin G (IgG) antibody responses, interleukin-4 (IL-4), and interferon gamma (IFN-γ) cytokine secretion. When using hollow MNs with cationic niosome/pOVA complexes, the immune response was superior to naked pOVA, which testifies the increased amount of IgG antibody responses and cytokine secretion. In comparison with conventional subcutaneous (SC) injections, using hollow MNs with cationic niosome/pOVA complexes induced a higher level of both IgG immune response and cytokine release. Moreover, a group of mice immunized with hollow MNs did not show infection or bleeding on the skin. Consequently, targeted delivery of pOVA using cationic niosomes combined with hollow MNs might prove a promising vaccination method for skin vaccination.

The viability and functionality of probiotics may be influenced by industrial production processes resulting in a decrease in probiotic efficiency that benefit the health of humans. This study aimed to investigate the probiotic characteristics of Bifidobacterium strains isolated from fecal samples of healthy Thai infants. In the present work, three local strains (BF014, BF052, and BH053) belonging to Bifidobacterium animalis showed a great resistance against conditions simulating the gastrointestinal tract. Among these, B. animalis BF052 possessed considerable probiotic properties, including high acid and bile tolerance, strong adhesion capability to Caco-2 cells, and inhibitory activity against pathogens including Salmonella typhimurium and Vibrio cholerae. This strain also exhibited a high survival rate compared to commercial strains during storage in a wide variety of products, including pasteurized milk, soy milk, drinking yogurt, and orange juice. The impact of food processing processes as well as the freeze-drying process, storage of freeze-dried powders, and incorporation of freeze-dried cells in food matrix on probiotic properties was also determined. The stability of the probiotic properties of the BF052 strain was not affected by food processing chain, especially its resistance in the simulated gastrointestinal conditions and its adherence ability to Caco-2 cells. It indicates that it satisfies the criteria as a potential probiotic and may be used as an effective probiotic starter in food applications.

No abstract

A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17–43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after thae trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.