Search

Group A rotavirus (RVA) is considered an important cause of acute gastroenteritis (AGE) in all age groups, especially in children. We investigated the epidemiology of RVA in outpatients aged ≤ 16 years at the Children's Hospital of Fudan University, Shanghai, China. In this study, 16.6% (246/1482) were infected with RVA. The detection rate of RVA was significantly higher in the year of 2021 (20.3%, 147/725) compared to the year of 2020 (14.5%, 77/531) and 2022 (9.7%, 22/226) (p = 0.000). RVA infection was prevalent in all seasons from 2020 to 2022, with a different monthly distribution observed in different years. Among 246 RVA‐positive samples, 14 different RVA genotypes were detected with different frequencies. Overall, G9P[8] (45.5%, 112/246) was the most common RVA genotype, followed by G8P[8] (37.4%, 92/246) and G3P[8] (4.1%, 10/246). The prevalence of G/P combinations varied from 2020 to 2022. G9P[8] was the most prevalent circulating genotype in 2020 (68.2%, 15/22) and 2021 (57.8%, 85/147). However, G8P[8] (68.8%, 53/77) suddenly became the most prevalent genotype in 2022 after being first identified in 2020 and prevalent in 2021. The G8 strains detected in the study were all clustered to DS‐1‐like G8 strains with the closest genetic distance to strains circulating in Southeast Asia. Our study demonstrated the diversity of circulating RVA genotypes in Shanghai. The sudden emergence and high prevalence of unusual G8P[8] strains deserve more concern and indicate the need for continuous surveillance of RVA in children with AGE in the future to refine future vaccine strategy. [ABSTRACT FROM AUTHOR]

Genome‐scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number (kcat ${k}_{\mathrm{cat}}$) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme‐constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme‐constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 kcat ${k}_{\mathrm{cat}}$c), highly sensitive (1759 kcat ${k}_{\mathrm{cat}}$), and nonsensitive (2502 kcat ${k}_{\mathrm{cat}}$) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy. [ABSTRACT FROM AUTHOR]

The anti-interference capability is vital for flexible wearable sensors, especially for detection during exercise. Herein, we propose a new sensing strategy based on the p–n transition potential (Vp–n) of the Bi2O3 photoelectrode. The Vp–n decreases linearly with pH and more importantly it demonstrates strong anti-interference performance against the change in the coverage of sweat on the photoelectrode, the variation in light intensity, and the bend of the photoelectrode. Moreover, the Bi2O3 photoelectrode shows good flexibility and biocompatibility, based on which a pH monitor that can interact with a smartphone is developed, enabling convenient and accurate monitoring of sweat pH during practical exercise. The monitor exhibits a benchmark signal-to-noise ratio that is one order of magnitude higher than that of the state of the art pH monitors. The strategy of using the Vp–n signal of a semiconductor for pH sensing is proposed for the first time, which opens a new avenue for designing anti-interference wearable sensors. [ABSTRACT FROM AUTHOR]