Cucurbitacin B modulates M2 macrophage differentiation and attenuates osteosarcoma progression via PI3K/AKT pathway.

Osteosarcoma is a common malignant bone tumour characterised by an aggressive metastatic potential. The tumour microenvironment, particularly the M2-polarised macrophages, is crucial for tumour progression. Cucurbitacin B (CuB), a triterpenoid derivative, is recognised for its anti-inflammatory and antitumour properties. This study investigates CuB and its effect on M2 macrophage differentiation and osteosarcoma progression, aiming to contribute to new treatment strategies. In vitro, THP-1 monocytes were stimulated with PMA, IL-13 and IL-4 to induce differentiation into M2 macrophages. Additionally, the influence of CuB on the proliferation, migration and invasion of osteosarcoma cells in the context of M2 macrophages was scrutinised. Crucial signalling pathways, especially the PI3K/AKT pathway, affected by CuB were identified and validated. In vivo, the osteosarcoma model was employed to gauge the effects of CuB on tumour weight, lung metastasis, angiogenesis, cell proliferation and M2 macrophage markers. The results showed that CuB inhibited M2 macrophage differentiation, leading to reduced proliferation, migration and invasion of osteosarcoma cells. CuB manifested an inhibitory effect on the PI3K/AKT pathway during the differentiation of M2 macrophages. In mouse models, CuB markedly reduced the tumour weight and the number of lung metastases. It also reduced the expression of angiogenesis and cell proliferation markers in tumour tissues, decreased the quantity of M2 macrophages and their associated markers and pathway proteins. In conclusion, CuB impedes osteosarcoma progression by inhibiting M2 macrophage differentiation via the PI3K/AKT pathway, presenting the potential for therapeutic advancements in osteosarcoma treatment.

Flight Delay Regression Prediction Model Based on Att-Conv-LSTM.

Accurate prediction results can provide an excellent reference value for the prevention of large-scale flight delays. Most of the currently available regression prediction algorithms use a single time series network to extract features, with less consideration of the spatial dimensional information contained in the data. Aiming at the above problem, a flight delay prediction method based on Att-Conv-LSTM is proposed. First, in order to fully extract both temporal and spatial information contained in the dataset, the long short-term memory network is used for getting time characteristics, and a convolutional neural network is adopted for obtaining spatial features. Then, the attention mechanism module is added to improve the iteration efficiency of the network. Experimental results show that the prediction error of the Conv-LSTM model is reduced by 11.41 percent compared with the single LSTM, and the prediction error of the Att-Conv-LSTM model is reduced by 10.83 percent compared with the Conv-LSTM. It is proven that considering spatio-temporal characteristics can obtain more accurate prediction results in the flight delay problem, and the attention mechanism module can also effectively improve the model performance.

Postmortem Distribution and Postmortem Redistribution of Carbofuran-7-Phenyl Glucuronic Acid in Rabbits.

OBJECTIVES: To establish a carbofuran intragastric administration death model in rabbits, and to observe the postmortem distribution and postmortem redistribution of carbofuran-7-phenyl glucuronic acid (Glu-7PH) in rabbits. METHODS: The postmortem distribution: Rabbits were given an administration of 1/2LD50, LD50, 2LD50 carbofuran. Dead rabbits were dissected immediately. Rabbits that had remained alive 2 hours were sacrificed by carbon dioxide (CO2) inhalation and dissected immediately. The myocardium, cardiac blood, liver, spleen, lung, kidney, brain and right hindlimb muscle were collected. The postmortem redistribution: After giving an administration of 4LD50 carbofuran, the myocardium, cardiac blood, liver, spleen, lung, kidney, brain, and right hindlimb muscle were collected at 0, 12, 24, 48, and 72 h postmortem in supine position at 15 ℃ room temperature. The quantity of Glu-7PH was determined by LC-MS/MS. RESULTS: The postmortem distribution: Among the three dose groups, there were significant differences in the quantities of Glu-7PH in different tissues. The postmortem redistribution: There was no significant difference in the Glu-7PH quantities in cardiac blood, mycardium, spleen, kidney, brain and right hindlimb muscle, but there was a significant difference in the Glu-7PH quantities in the liver and lung. CONCLUSIONS: The mycardium, cardiac blood, liver, lung, kidney, brain and hindlimb muscle of rabbits can be used as appropriate samples for Glu-7PH detection. However, it should be noted that Glu-7PH was redistributed postmortem in rabbit liver and lung.

EPAT: a user-friendly MATLAB toolbox for EEG/ERP data processing and analysis.

Background: At the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application. Methods: We introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality. Results: EPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT. Conclusion: This article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.