Nanobubbles have received great attention in ultrasound molecular imaging due to their capability to pass through the vasculature and reach extravascular tissues. Recently, gas vesicles (GVs) from archaea have been reported as acoustic contrast agents, showing great potential for ultrasound molecular imaging. However, the immunogenicity and biosafety of GVs has not yet been investigated. In this study, we examined the immune responses and biosafety of biosynthetic GVs and polyethylene glycol (PEG)-modified GVs (PEG-GVs) in vivo and in vitro. Our findings suggest that the plain GVs showed significantly stronger immunogenic response than PEG-GVs. Less macrophage clearance rate of the RES and longer circulation time were also found for PEG-GVs, thereby producing the better contrast imaging effect in vivo. Thus, our study demonstrated the PEG modification of biosynthetic GVs from Halobacterium NRC-1 is helpful for the future application of GVs in molecular imaging and treatment.

Due to the complex structure, nonlinearity and tolerance of IF conversion circuit, the failure probability of IF conversion circuit is greatly increased. To solve the above problems, a differential evolution adaptive grey wolf algorithm (DE-GWO) was proposed to optimize the parameters of support vector machine model, and the model was applied to the fault diagnosis of IF conversion circuit for the first time. Firstly, the energy feature of the output signal of IF conversion circuit was extracted by wavelet packet decomposition, which effectively reduced the dimension of feature vector. Secondly, the improved grey wolf algorithm was used to optimize the parameters of SVM model and establish DE-GWO-SVM fault diagnosis model; Finally, taking the IF conversion circuit as an example, the fault diagnosis experiment was carried out and compared with other methods. Comparison results show that the fault diagnosis accuracy rate of this method reaches 97.33%. Compared with the traditional methods, this method can better improve the fault diagnosis rate and shorten the diagnosis time.

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