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IntroductionRadioresistance remains a significant challenge in the treatment of glioblastoma multiforme (GBM), the most prevalent and lethal brain cancer in adults. Metabolic alterations are known to contribute to radioresistance by activating antioxidant responses and promoting DNA repair. However, the role of circular RNAs in this process, particularly circFOXO3, is not well understood.MethodsIn this study, we investigated the expression of circFOXO3 in glioma cells exposed to radiation and in recurrent GBM tissues. We performed knockdown and overexpression experiments in vitro and in vivo to assess the effects of circFOXO3 on radiosensitivity. Metabolomic profiling was conducted to explore the metabolic changes associated with circFOXO3 overexpression following irradiation.ResultsOur results showed significant upregulation of circFOXO3 in glioma cells upon radiation exposure and in recurrent GBM tissues. Knockdown of circFOXO3 increased radiosensitivity both in vitro and in vivo, whereas overexpression of circFOXO3 attenuated radiosensitivity. Metabolomic analysis revealed substantial alterations in lipid and organic compound profiles between circFOXO3-overexpressing and control groups. Additionally, circFOXO3 suppression increased proapoptotic protein levels (Caspase 7 and Bax) and decreased anti-apoptotic protein Bcl-2 levels following radiotherapy.DiscussionThese findings demonstrate the pivotal role of circFOXO3 in promoting tumor radioresistance through metabolic modulation, suggesting that circFOXO3 could serve as a potential diagnostic and therapeutic target for GBM.

Starch, lipids, and proteins are key macronutrients in starchy foods. Their interactions during processing can form starch-lipid-protein ternary complexes, significantly affecting food quality. Ultrasonic treatment, as a common processing method, is expected to regulate the quality of starchy foods by influencing the formation of ternary complexes. This study aimed to understand the effect of ultrasonic pretreatment on the formation of starch-lipid-protein ternary complexes using various types of starches. Wheat starch (WS), maize starch (MS), and potato starch (PS) were gelatinized and treated with ultrasound at various power densities (0–40 W/L) to form complexes with lauric acid (LA) and β-lactoglobulin (βLG), respectively. Ultrasound increased the amylose content of gelatinized WS, MS, and PS and shifted their chain length distribution towards the short chains. Results from Fourier transform infrared spectroscopy, laser confocal micro-Raman, X-ray diffraction, and differential scanning calorimetry showed that the largest amount of WS-LA-βLG complexes was formed at the ultrasonic power density of 10 W/L, and MS-LA-βLG and PS-LA-βLG complexes at 20 W/L. Additionally, ultrasound enhanced the content of resistant starch (RS) in the starch-LA-βLG complexes. The RS content increased from 14.12 % to 18.31 % for WS-LA-βLG, and from 19.18 % and 20.69 % to 27.60 % and 28.63 % for MS-LA-βLG and PS-LA-βLG complexes, respectively. This study presents an approach for facilitating the formation of ternary complexes, contributing to the development of low-GI functional foods.

IntroductionGlioblastoma multiforme (GBM), the most common primary malignant brain tumor, is notorious for its aggressive growth and dismal prognosis. This study aimed to elucidate the molecular underpinnings of GBM, particularly focusing on the role of AGBL4 and its connection to inflammatory pathways, to discover viable therapeutic targets.MethodsSingle-cell sequencing was utilized to examine the expression levels of AGBL4 and functional assays were performed to assess the effects of AGBL4 modulation.ResultsOur findings identified the significant upregulation of AGBL4 in GBM, which correlated with adverse clinical outcomes. Functional assays demonstrated that AGBL4 knockdown inhibited GBM cell proliferation, migration, and invasion and influenced inflammatory response pathways, while AGBL4 overexpression promoted these activities. Further investigation revealed that AGBL4 exerted its oncogenic effects through modulation of MMP-1, establishing a novel regulatory axis critical for GBM progression and inflammation.DiscussionBoth AGBL4 and MMP-1 may be pivotal molecular targets, offering new avenues for targeted therapy in GBM management.

This study evaluated the ability of a high-voltage electrostatic field (HVEF) treatment to extend the shelf life of tomatoes. Tomatoes were exposed to HVEF treatment for different lengths of time, and the physicochemical properties of tomatoes and bioactive compounds were monitored during 28 days of storage at 4 °C. The results indicated that the quality parameters of tomatoes were better maintained during storage by the HVEF treatment relative to the control treatment, extending their shelf life by 14–28 days. The HVEF treatment mitigated losses in firmness, weight, color changes, and bioactive substances, such as total phenolic content, total flavonoid content, ascorbic acid, and lycopene. The activity of pectin-degrading enzymes was also inhibited. The best exposure times for the HVEF treatment were 90 and 120 min. While the measured parameters decreased in both the control and HVEF treatment groups, the decrease in all of these measured parameters was significantly less (p < 0.05) in the optimum HVEF treatment groups than in the control. While the physicochemical properties may vary between different tomato varieties, the HVEF treatment of harvested tomatoes for 90 or 120 min can mitigate the degradation of quality parameters and loss of bioactive compounds incurred during the postharvest storage of tomatoes, thus maintaining their commercial value.

Abstract Background Malignant glioma is one of the essentially incurable tumors with chemoresistance and tumor recurrence. As a histone methyltransferase, SUV39H2 can trimethylate H3K9. SUV39H2 is highly expressed in many types of human tumors, while the function of SUV39H2 in the development and progression of glioma has never been elucidated. Methods RT-qPCR and IHC were used to test SUV39H2 levels in glioma tissues and paired normal tissues. The clinical relevance of SUV39H2 in glioma was analyzed in a public database. Colony formation assays, CCK-8 assays, and flow cytometry were conducted to explore the role of SUV39H2 in the growth of glioma cells in vitro. A cell line-derived xenograft model was applied to explore SUV39H2’s role in U251 cell proliferation in vivo. Sphere formation assays, RT-qPCR, flow cytometry, and IF were conducted to illustrate the role of SUV39H2 in the stemness and chemosensitivity of glioma. Luciferase reporter assays and WB were applied to determine the function of SUV39H2 in Hh signaling. Results SUV39H2 was highly expressed in glioma tissues relative to normal tissues. SUV39H2 knockdown inhibited cell proliferation and stemness and promoted the chemosensitivity of glioma cells in vitro. In addition, SUV39H2 knockdown also significantly inhibited glioma cell growth in vivo. Moreover, we further uncovered that SUV39H2 regulated hedgehog signaling by repressing HHIP expression. Conclusions Our findings delineate the role of SUV39H2 in glioma cell growth and chemosensitivity as a pivotal regulator of the hedgehog signaling pathway and may support SUV39H2 as a potential target for diagnosis and therapy in glioma management.

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and stability of power system. In this study, we propose an ensemble model for probabilistic wind speed forecasting. It consists of wavelet threshold denoising (WTD), recurrent neural network (RNN) and adaptive neuro fuzzy inference system (ANFIS). Firstly, WTD smooths the wind speed series in order to better capture its variation trend. Secondly, RNNs with different architectures are trained on the denoising datasets, operating as sub-models for point wind speed forecasting. Thirdly, ANFIS is innovatively established as the top layer of the entire ensemble model to compute the final point prediction result, in order to take full advantages of a limited number of deep-learning-based sub-models. Lastly, variances are obtained from sub-models and then prediction intervals of probabilistic forecasting can be calculated, where the variances inventively consist of modeling and forecasting uncertainties. The proposed ensemble model is established and verified on less than one-hour-ahead ultra-short-term wind speed forecasting. We compare it with other soft computing models. The results indicate the feasibility and superiority of the proposed model in both point and probabilistic wind speed forecasting.

Photovoltaic (PV) electric power has been widely employed to satisfy rising energy demands because inexhaustible renewable energy is environmentally friendly. In order to mitigate the impact caused by the uncertainty of solar radiation in grid-connected PV systems, a hybrid method based on a deep convolutional neural network (CNN) is introduced for short-term PV power forecasting. In the proposed method, different frequency components are first decomposed from the historical time series of PV power through variational mode decomposition (VMD). Then, they are constructed into a two-dimensional data form with correlations in both daily and hourly timescales that can be extracted by convolution kernels. Moreover, the time series of residue from VMD is refined into advanced features by a CNN, which could reduce the data size and be easier for further model training along with meteorological elements. The hybrid model has been verified by forecasting the output power of PV arrays with diverse capacities in various hourly timescales, which demonstrates its superiority over commonly used methods. [ABSTRACT FROM AUTHOR]