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- 저자 : Mengqi Lv (검색결과 2 건)
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Yulu Sun,Guijun Hao,Mengqi Zhuang,Huijuan Lv,Chunhong Liu,Keli Su 연세대학교의과대학 2022 Yonsei medical journal Vol.63 No.3
Purpose: Long non-coding RNAs (lncRNAs) may act as oncogenes in small-cell lung cancer (SCLC). Exosomes containing lncRNAsreleased from cancer-associated fibroblasts (CAF) accelerate tumorigenesis and confer chemoresistance. This studyaimed to explore the action mechanism of the CAF-derived lncRNA maternally expressed gene 3 (MEG3) on cisplatin (DDP) chemoresistanceand cell processes in SCLC. Materials and Methods: Quantitative real-time PCR was conducted to determine the expression levels of MEG3, miR-15a-5p, andCCNE1. Cell viability and metastasis were measured by 3-(4, 5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2-h-tetrazolium bromide andinvasion assays, respectively. A xenograft tumor model was developed to confirm the effect of MEG3 overexpression on SCLC progressionin vivo. Relationships between miR-15a-5p and MEG3/CCNE1 were predicted using StarBase software and validated bydual luciferase reporter assay. Western blotting was used to determine protein levels. A co-culture model was established to explorethe effects of exosomes on MEG3 expression in SCLC cell lines. Results: MEG3 was overexpressed in SCLC tissues and cells. MEG3 silencing significantly repressed cell viability and metastasis inSCLC. High expression of MEG3 was observed in CAF-derived conditioned medium (CM) and exosomes, and promoted chemoresistanceand cancer progression. Additionally, MEG3 was found to serve as a sponge of miR-15a-5p to mediate CCNE1 expression. Overexpression of miR-15a-5p and knockout of CCNE1 reversed the effects of MEG3 overexpression on cell viability and metastasis. Conclusion: MEG3 lncRNA released from CAF-derived exosomes promotes DDP chemoresistance via regulation of a miR-15a-5p/CCNE1 axis. These findings may provide insight into SCLC therapy.
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The Impact of the Parking Spot’ Surroundings on Charging Decision: A Data-Driven Approach
Xizhen Zhou,Yanjie Ji,Mengqi Lv 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.5
The charging behavior of drivers serves as a valuable reference for planning and managing charging facilities. This study examines the influence of surrounding environments on charging decisions using real trajectory data from electric vehicles. It considers the built environment, vehicle conditions, and the nearest charging station attributes. The mixed binary logit model was applied to capture the impact of unobserved heterogeneity. The findings indicate that the number of fast chargers in the charging station, parking prices, dwell time, and shopping services significantly influence charging decisions, while leisure services, scenic spots, and mileage since the last charging exhibit opposite effects. Additionally, factors related to unobserved heterogeneity include the number of fast chargers, parking and charging prices, and residential areas. The interaction effects of random parameters further illustrate the complexity of charging choice behavior. Overall, the results offer valuable insights for the planning and management of charging facilities.
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