The notion that widespread infectious diseases could be best managed by developing potent, adjuvant-free vaccines has resulted in the use of various biological immune-stimulating components as new vaccine candidates. Recently, extracellular vesicles, also known as exosomes and microvesicles in mammalian cells and outer membrane vesicles in Gram-negative bacteria, have gained attention for the next generation vaccine. However, the more invasive and effective the vaccine is in delivery, the more risk it holds for severe immune toxicity. Here, in optimizing the current vaccine delivery system, we designed bacterial protoplast-derived nanovesicles (PDNVs), depleted of toxic outer membrane components to generate a universal adjuvant-free vaccine delivery system. These PDNVs exhibited significantly higher productivity and safety than the currently used vaccine delivery vehicles and induced strong antigen-specific humoral and cellular immune responses. Moreover, immunization with PDNVs loaded with bacterial antigens conferred effective protection against bacterial sepsis in mice. These nonliving nanovesicles derived from bacterial protoplast open up a new avenue for the creation of next generation, adjuvant-free, less toxic vaccines to be used to prevent infectious diseases.

In this study, the two-phase pressure drop of a one-side heated swirl tube (twist ratio=3, 5) was experimentally studied. If the a heat flux is gradually increased, the vapor blocks the flow path, thus leading to an increase in the differential pressure. As the inset sub-cooling and mass flow rate increase, the vapor is removed by condensation more rapidly, and thus, the rate of increase of differential pressure is reduced. From the evaluation of the prediction performance of the existing two-phase pressure drop correlations, it was found that the mean error rates of all correlations exceeded 50%. This is because most of the existing correlations were not developed based on the microchannel, but they were not focused on the swirl tube. Therefore, the authors developed a new two-phase pressure drop multiplier correlation for a one-side heated swirl tube that reflects the effect of the twist tape by modifying the Manglick single-phase swirl tube pressure drop correlation using the AI regression method.

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