Bioactive sesquiterpenoids and sesquiterpenoid glucosides from the flowers of Inula japonica

Yu, Zhi-Pu; Zhang, Jun‐Sheng; Zhang, Qianqian; Yu, Shujuan; Zhang, Yuying; Yu, Jin‐Hai; Zhang, Hua

doi:10.1016/j.fitote.2019.104292

Cited by 15 publications

(2 citation statements)

References 46 publications

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“…Current pharmacology researches had showed that I. japonica had significant anticancer effects [7] . A lot of sesquiterpenoid derivatives were isolated and identified, which are believed to the active components of the plant species [8–11] . 1,10‐ Seco ‐ eudesmanolides are a class of rare naturally occurring sesquiterpenoids found from the Inula plants [12] .…”

Section: Introductionmentioning

confidence: 99%

Cytotoxic Terpenes from the Flowers of Inula japonica against Human Hepatocarcinoma Cells

Wang

Chen

Zhang

et al. 2023

Chemistry & Biodiversity

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Two new 1,10‐seco‐eudesmanolides (1 and 2) were isolated from the flowers of Inula japonica together with two eudesmanolide analogs (3 and 4) and two monoterpene derivatives (5 and 6). Their structures were established on the basis of detailed spectroscopic analyses and electronic circular dichroism data. All isolates were evaluated for their antiproliferative activities against human hepatocarcinoma HepG2 and SMMC‐7721 cells. Japonipene B (3) exhibited the most potent effect with the IC50 values of 14.60±1.62 and 22.06±1.34 μM against HepG2 and SMMC‐7721 cells, respectively. Furthermore, japonipene B (3) showed significant efficacies of arresting the cell cycle at the S/G2‐M stages, inducing mitochondria‐mediated apoptosis, and inhibiting cell migration in HepG2 cells.

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Section: Introductionmentioning

confidence: 99%

Cytotoxic Terpenes from the Flowers of Inula japonica against Human Hepatocarcinoma Cells

Wang

Chen

Zhang

et al. 2023

Chemistry & Biodiversity

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“…At present, some foreign scholars have already used algorithms such as genetic algorithm (GA) [28], artificial neural network (ANN) [29] and adaptive neuro-fuzzy inference system (ANFIS) [30] for the calculation, prediction and optimal design of heat transfer and pressure drop of heat exchangers, and few domestic ones are currently using machine learning algorithms to optimize vehicle power performance [31][32][33][34][35][36]. The purpose of this paper is to propose an optimization method for louvered window fin heat exchangers, i.e., to use artificial neural network algorithms to optimize the lou-vered window fin structure and find the optimal fin structure parameters in order to reduce unnecessary repetitive modeling and simulation, accelerate the optimal design of heat exchangers, and achieve overall optimization with few samples.…”

Section: Introductionmentioning

confidence: 99%

Pre-research on enhanced heat transfer method for special vehicles at high altitude based on machine learning

Sun

Gao

et al. 2022

Preprint

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The performance of the thermal management system has a great influence on the stability of the special purpose vehicles, and it is of great significance to enhance heat transfer of the radiator. Common research methods for radiators include fluid mechanics numerical simulation and experimental measurements, both of which are time-consuming and expensive. Applying the surrogate model to the analysis of flow and heat transfer in louver fin can effectively reduce the computational cost and obtain more data. A simplified louver fin heat transfer unit is established, and computational fluid dynamics (CFD) simulations is used to obtain the flow and heat transfer characteristics of this geometry structure. A three-factor and six-level orthogonal design is carried out with three structural parameters, the angle θ, the length a and the spacing Lp of the louver fins. The results of the orthogonal design are subjected to range analysis, and the effects of the three parameters θ, a and Lp on the j, f and JF factors are obtained. On this basis, a proxy model of the heat transfer performance for louver fins was established based on the artificial neural network algorithm, and the model was trained with the data obtained by the orthogonal design, and finally the fin structure with the largest JF factor was found. Compared with the original model, the optimized model improves the heat transfer factor j by 2.87%, the friction factor f decreases by 30.4% and the comprehensive factor JF increases by 15.7%.

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Japonicone A and related dimeric sesquiterpene lactones: molecular targets and mechanisms of anticancer activity

Bailly

Vergoten

2022

Inflamm. Res.

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Bioactive sesquiterpenoids and sesquiterpenoid glucosides from the flowers of Inula japonica

Cited by 15 publications

References 46 publications

Cytotoxic Terpenes from the Flowers of Inula japonica against Human Hepatocarcinoma Cells

Cytotoxic Terpenes from the Flowers of Inula japonica against Human Hepatocarcinoma Cells

Pre-research on enhanced heat transfer method for special vehicles at high altitude based on machine learning

Japonicone A and related dimeric sesquiterpene lactones: molecular targets and mechanisms of anticancer activity

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