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Abstract Background To identify independent factors of cognitive frailty (CF) and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy. Methods In this cross‐sectional study, patients with lung cancer undergoing drug therapy from October 2022 to July 2023 were enrolled. The data collected includes general demographic characteristics, clinical data characteristics and assessment of tools for cognitive frailty and other factors. Logistic regression was harnessed to determine the influencing factors, R software was used to establish a nomogram model to predict the risk of cognitive frailty. The enhanced bootstrap method was employed for internal verification of the model. The performance of the nomogram was evaluated by using calibration curves, the area under the receiver operating characteristic curve, and decision curve analysis. Results A total of 372 patients were recruited, with a cognitive frailty prevalence of 56.2%. Age, education background, diabetes mellitus, insomnia, sarcopenia, and nutrition status were identified as independent factors. Then, a nomogram model was constructed and patients were classified into high‐ and low‐risk groups with a cutoff value of 0.552. The internal validation results revealed good concordance, calibration and discrimination. The decision curve analysis presented prominent clinical utility. Conclusions The prevalence of cognitive frailty was higher in lung cancer patients receiving drug therapy. The nomogram could identify the risk of cognitive frailty intuitively and simply in patients with lung cancer, so as to provide references for early screening and intervention for cognitive frailty at the early phases of drug treatment.

Abstract As a signaling molecule, nitric oxide (NO) regulates the development and stress response in different organisms. The major biological activity of NO is protein S‐nitrosylation, whose function in fungi remains largely unclear. Here, it is found in the rice blast fungus Magnaporthe oryzae, de‐nitrosylation process is essential for functional appressorium formation during infection. Nitrosative stress caused by excessive accumulation of NO is harmful for fungal infection. While the S‐nitrosoglutathione reductase GSNOR‐mediated de‐nitrosylation removes excess NO toxicity during appressorium formation to promote infection. Through an indoTMT switch labeling proteomics technique, 741 S‐nitrosylation sites in 483 proteins are identified. Key appressorial proteins, such as Mgb1, MagB, Sps1, Cdc42, and septins, are activated by GSNOR through de‐nitrosylation. Removing S‐nitrosylation sites of above proteins is essential for proper protein structure and appressorial function. Therefore, GSNOR‐mediated de‐nitrosylation is an essential regulator for appressorium formation. It is also shown that breaking NO homeostasis by NO donors, NO scavengers, as well as chemical inhibitor of GSNOR, shall be effective methods for fungal disease control.

Since the model parameters of the shaking table exist in a non-linear form, this leads to distortion of the reproduced waveforms and can even lead to bias in the ground vibration test results. Therefore, the selection of the controller is particularly critical. Multi-variable (MVC) controllers are often used in shaking table control, to improve the control effect of MVC controllers. In this paper, a multi-parametric (BP-MVC) controller based on BP neural network is proposed. The BP neural network is applied to the multi-parameter (MVC) controller to identify the shaking table model, adjust the parameters in real-time, accelerate the convergence speed, and reduce the system error. The simulation results show that the correlation coefficient (CC) of the BP-MVC controller is greater than 0.985, and the root-mean-square error (RMSE) and mean absolute error (MAE) are less than 0.04 and 0.25, respectively, in a nonlinear, time-varying hydraulic system. This suggests that the BP-MVC controller has a better control performance and parameter adaptivity, which can provide a reference for the subsequent ground vibration tests.

Monitoring the annual variation of antibiotic resistance genes (ARGs) in livestock wastewater is important for determining the high-risk period of transfer and spread of animal-derived antibiotic resistance into the environment. However, the knowledge regarding the variation patterns of ARGs, especially intracellular ARGs (iARGs) and extracellular ARGs (eARGs), over time in livestock wastewater is still unclear. Herein, we conducted a year-round study to trace the profiles of ARGs at a Chinese-intensive dairy farm, focusing on the shifts observed in different months. The results showed significant differences in the composition and variation between iARGs and eARGs. Tetracycline, sulfonamide, and macrolide resistance genes were the major types of iARGs, while cfr was the major type of eARG. The environmental adaptations of the host bacteria determine whether ARGs appear as intracellular or extracellular forms. The total abundance of ARGs was higher from April to September, which can be attributed to the favorable climatic conditions for bacterial colonization and increased antibiotic administration during this period. Integron was found to be highly correlated with most iARGs, potentially playing a role in the presence of these genes within cells and their similar transmission patterns in wastewater. The intracellular and extracellular bacterial communities were significantly different, primarily because of variations in bacterial adaptability to the high salt and anaerobic environment. The intracellular co-occurrence network indicated that some dominant genera in wastewater, such as Turicibacter, Clostridium IV, Cloacibacillus, Subdivision5_genera_incertae_sedis, Saccharibacteria_genera_incertae_sedis and Halomonas, were potential hosts for many ARGs. To the best of our knowledge, this study demonstrates, for the first time, the annual variation of ARGs at critical points in the reuse of dairy farm wastewater. It also offers valuable insights into the prevention and control of ARGs derived from animals.