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.

Fighting wheat powdery mildew: from genes to fields.

KEY MESSAGE: Host resistance conferred by Pm genes provides an effective strategy to control powdery mildew. The study of Pm genes helps modern breeding develop toward more intelligent and customized. Powdery mildew of wheat is one of the most destructive diseases seriously threatening the crop yield and quality worldwide. The genetic research on powdery mildew (Pm) resistance has entered a new era. Many Pm genes from wheat and its wild and domesticated relatives have been mined and cloned. Meanwhile, modern breeding strategies based on high-throughput sequencing and genome editing are emerging and developing toward more intelligent and customized. This review highlights mining and cloning of Pm genes, molecular mechanism studies on the resistance and avirulence genes, and prospects for genomic-assisted breeding for powdery mildew resistance in wheat.

Bayesian network-based missing mechanism identification (BN-MMI) method in medical research.

BACKGROUND: Traditional approaches to identify missing mechanisms are usually based on the hypothesis test and confronted with both theoretical and practical challenges. It has been proved that the Bayesian network is powerful in integrating, analyzing and visualizing information, and some previous researches have verified the promising features of Bayesian network to deal with the aforementioned challenges in missing mechanism identification. Based on the above reasons, this paper explores the application of Bayesian network to the identification of missing mechanisms for the first time, and proposes a new method, the Bayesian network-based missing mechanism identification (BN-MMI) method, to identify missing mechanism in medical research. METHODS: The procedure of BN-MMI method consists three easy-to-implement steps: estimating the missing data structure by the Bayesian network; assessing the credibility of the estimated missing data structure; and identifying the missing mechanism from the estimated missing data structure. The BN-MMI method is verified by simulation research and empirical research. RESULTS: The simulation study verified the validity, consistency and robustness of BN-MMI method, and indicated its outperformance in contrast to the traditional logistic regression method. In addition, the empirical study illustrated the applicability of BN-MMI method in the real world by an example of medical record data. CONCLUSIONS: It was confirmed that the BN-MMI method itself, together with human knowledge and expertise, could identify the missing mechanisms according to the probabilistic dependence/independence relations among variables of interest. At the same time, our research shed light upon the potential application of BN-MMI method to a broader range of missing data issues in medical studies.

[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.