Silver Nanoparticles Encapped by Dihydromyricetin: Optimization of Green Synthesis, Characterization, Toxicity, and Anti-MRSA Infection Activities for Zebrafish (Danio rerio).

To achieve the environmentally friendly and rapid green synthesis of efficient and stable AgNPs for drug-resistant bacterial infection, this study optimized the green synthesis process of silver nanoparticles (AgNPs) using Dihydromyricetin (DMY). Then, we assessed the impact of AgNPs on zebrafish embryo development, as well as their therapeutic efficacy on zebrafish infected with Methicillin-resistant Staphylococcus aureus (MRSA). Transmission electron microscopy (TEM) and dynamic light-scattering (DLS) analyses revealed that AgNPs possessed an average size of 23.6 nm, a polymer dispersity index (PDI) of 0.197 ± 0.0196, and a zeta potential of -18.1 ± 1.18 mV. Compared to other published green synthesis products, the optimized DMY-AgNPs exhibited smaller sizes, narrower size distributions, and enhanced stability. Furthermore, the minimum concentration of DMY-AgNPs required to affect zebrafish hatching and survival was determined to be 25.0 µg/mL, indicating the low toxicity of DMY-AgNPs. Following a 5-day feeding regimen with DMY-AgNP-containing food, significant improvements were observed in the recovery of the gills, intestines, and livers in MRSA-infected zebrafish. These results suggested that optimized DMY-AgNPs hold promise for application in aquacultures and offer potential for further clinical use against drug-resistant bacteria.

[Missing Data Replacement Methods in Different Scenarios].

OBJECTIVE: To compare the effect of different approaches of missing data replacement on the regression coefficient estimates r of "length of stay" on "hospital expenditure". METHODS: Data were extracted from the medical records of patients with head and neck neoplasms who were admitted to Sichuan Cancer Hospital. R 3.4.1 was used for generating and processing simulated datasets. Various scenarios were established by setting up different proportions of missing data and missing mechanisms using Monte Carlo method. Three strategies were tested for replacing missing data: Complete Case method,Expectation Maximization (EM),and Markov Chain Monte Carlo method (MCMC). The regression coefficient estimates r of standardized "length of stay" on standardized logarithmic "hospital expenditure" were calculated using these strategies and compared with that of the original complete dataset,in terms of their accuracy (magnitude of differences in r) and precision (differences in the standard error of r). RESULTS: The three replacement methods were all acceptable (within the limit rc±0.5 sc) when missing data were generated using MAR (2∶1) mechanism,or less than 30% data were simulated as missing using the MCAR and MAR (1∶2) mechanism. The EM method had the best estimation precision. CONCLUSION: Missing data replacement should consider the proportion of missing data and potential mechanisms involved.