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Background Osteosarcoma (OS) is the most prevalent orthopedic malignancy with a dismal prognosis. The high iron absorption rate in OS cells of patients suggests that ferroptosis may be related to the progression of OS, but its potential molecular regulatory role is still unclear. Based on the ability to couple with exosomes for targeted delivery of signals, exosome-derived micro ribonucleic acids (miRNAs) can potentially serve as diagnostic biomarkers for OS. Methods We identified ferroptosis-related miRNAs and messenger ribonucleic acids(mRNAs) in OS using bioinformatics analysis and performed survival analysis. Then we measured miRNA expression levels through exosome microarray sequencing, and used RT-qPCR and IHC to verify the expression level of miR-144-3p and ZEB1. Stable gene expression cell lines were fabricated for in vitro experiments. Cell viability, migration and invasion were determined by CCK-8 and transwell experiment. Use the corresponding reagent kit to detect GSH/GSSG ratio, Fe2+ level, MDA level and ROS level, and measure the expression levels of GPX4, ACSL4 and xCT through RT-qPCR and WB. We also constructed nude mice model for in vivo experiments. Finally, the stability of the miRNA/mRNA axis was verified through functional rescue experiments. Results Low expression of miR-144-3p and high expression of ZEB1 in OS cell lines and tissues was observed. Overexpression of miR-144-3p can promote ferroptosis, reduce the survival ability of OS cells, and prevent the progression of OS. In addition, overexpression of miR-144-3p can downregulate the expression of ZEB1 in cell lines and nude mice. Knockdown of miR-144-3p has the opposite effect. The functional rescue experiment validated that miR-144-3p can regulate downstream ZEB1, and participates in the occurrence and development of OS by interfering with redox homeostasis and iron metabolism. Conclusions MiR-144-3p can induce the occurrence of ferroptosis by negatively regulating the expression of ZEB1, thereby inhibiting the proliferation, migration, and invasion of OS cells. Graphical Abstract

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With the rapid growth of energy consumption, how to utilize energy in an efficient and cheap way becomes an intensive problem. This paper proposes an optimal operation strategy to reduce system fuel costs and increase system stability by independently considering cooling loads and adjusting CHP heat to power ratio seasonally. In this paper, a mathematical model of CHP operation is introduced to reveal the relationship between the supplementary volume of diesel oil and CHP heat to power ratio. Meanwhile, by analyzing the influence of seasonal factor on energy consumption, CHP heat to power ratio is optimized seasonally. Then, by independently considering the impacts of the cooling loads on system operation, the particle swarm optimization (PSO) algorithm is used to optimize the operation strategy of each device. Finally, this paper validates the positive effects of storage devices on improving system economy and stability under the premise of the time-of-use gas price. Results show that system fuel costs can be reduced by 5.2% if the seasonal factor is considered. Additionally, by optimizing the operation strategy, the peak valley gap of electrical loads in summer reduces by 40.7%. Moreover, the proposed strategy successfully utilizes storage capacity to shift loads and respond to gas price.

Clean and low-carbon electricity-gas integrated energy system (EGIES) is being developed rapidly in order to meet the dual-carbon target. Situation awareness can provide an early warning of operational risks to the EGIES, which is helpful for its promotion and application. In this paper, a data-driven situation awareness method of EGIES considering time series features is proposed. The state and deviation vectors of EGIES are solved at the situation perception level based on the state estimation. The recurrence plot (RP) is used at the situation comprehension level to extract the time series features of historical deviations, and the operating state of future deviations is encoded in the form of labels. A convolutional neural network (CNN) is established at the situation projection level to project the future operating state of the EGIES based on the RP of the historical deviations. A case study of EGIES coupling a 14-node power system with a 7-node gas system shows that the projection accuracy of the proposed method increases by 2.07 and 3.04% compared with the long-short term memory (LSTM) neural network and the support vector machine (SVM), respectively.

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