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Organoid culture is a promising biomedical technology that requires specialized growth factors. Recently, a recombinant L-WRN cell line has been extensively used to generate conditioned medium (L-CM) for organoid culture. Nevertheless, methods for evaluating the stability of the L-WRN cells have been limited. In this study, a novel proteomics-based approach was developed to analyze the secretome of the cells. Serum-free L-CM was lyophilized, precipitated by trichloroacetic acid, and desalted prior to analysis by liquid chromatography–tandem mass spectrometry. Data-dependent acquisition (DDA) was conducted for the untargeted secretome profiling of the cells, and parallel reaction monitoring (PRM) was applied for the targeted quantification of the Wnt3A, R-spondin3, and noggin proteins (WRNs). This study also compared the performance of two types of PRM methods, namely MS1-independent PRM and MS1-dependent PRM, that can be executed on an Orbitrap instrument. The results showed that the growth of mouse intestinal organoids was closely related to the use of L-CM. The composition of L-CM could be markedly affected by the medium collection scheme. A total of 1725, 2302, and 2681 proteins were identified from the L-CM collected on day 5, day 9, and day 13, respectively. The MS1-independent PRM outperformed the MS1-dependent PRM and effectively quantified the WRNs with high repeatability and specificity. In conclusion, by integrating untargeted and targeted proteomics, this study develops a mass spectrometry–based method for the secretome analysis and quality control of the L-WRN cells. The methodology and findings of the present work will benefit future studies on organoids and secretomes. [ABSTRACT FROM AUTHOR]

The accuracy level of short-term load forecasting (STLF) affects the power department's arrangements for unit start-up, shutdown, overhaul, and load dispatching. However, the existing algorithms do not fully consider load volatility and difficulty in setting the algorithm parameters. In this regard, this paper proposes a hybrid model which combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU) with improved gray wolf optimizer (IGWO), namely CEEMDAN–IGWO–GRU (CIG) hybrid algorithm. Firstly, the details and trend information of the load signal are separated by the CEEMDAN algorithm to suppress the interference of load fluctuation. Then, the GRU network optimized by IGWO parameters is used to predict each component, separately. Finally, the complete load forecasting results are obtained by reconstructing the forecasting result of each component. The power load data of a certain area under study is used to verify the CIG model, and the experimental results are compared with other existing algorithms. The experimental results show that the load forecasting results of the CIG model get the high-precision evaluation of 0.6997%, 52.4685, and 38.1891 MW in MAPE, RMSE, and MAE, respectively. Therefore, the parameter optimization ability of the IGWO algorithm can effectively improve the prediction accuracy, and the CIG method can availably suppress the impact of load fluctuation on prediction and has a powerful nonlinear fitting ability. In conclusion, CIG has great potential in establishing a power load forecasting model. [ABSTRACT FROM AUTHOR]