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Semi-Mechanism-Based Pharmacodynamic Model for the Anti-Inflammatory Effect of Baicalein in LPS-Stimulated RAW264.7 Macrophages

Overview of attention for article published in Frontiers in Pharmacology, July 2018
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Title
Semi-Mechanism-Based Pharmacodynamic Model for the Anti-Inflammatory Effect of Baicalein in LPS-Stimulated RAW264.7 Macrophages
Published in
Frontiers in Pharmacology, July 2018
DOI 10.3389/fphar.2018.00793
Pubmed ID
Authors

Li Xiang, Ying-Fan Hu, Jia-Si Wu, Li Wang, Wen-Ge Huang, Chen-Si Xu, Xian-Li Meng, Ping Wang

Abstract

Monitoring of the inhibition of TNF-α, IL-6, iNOS, and NO is used to effectively evaluate anti-inflammatory drugs. Baicalein was found to have good anti-inflammatory activities, but its detailed cellular pharmacodynamic events have not been expatiated by any other study. The inflammatory mediators, including TNF-α, IL-6, iNOS, and NO production in RAW264.7 macrophage induced by LPS, were measured. It was found that these data showed a sequential pattern on time and based on these points a cellular pharmacodynamic model was developed and tested. TNF-α and IL-6 were quantified by ELISA, NO was detected by Griess and iNOS expression was measured by Western blot. The pharmacodynamic model was developed using a NLME modeling program Monolix® 2016R1.The results showed that baicalein quickly suppressed release of TNF-α in a concentration-dependent manner, and consequently causing the diminution of IL-6 and iNOS/NO. The pharmacodynamic model simulation successfully described the experimental data, supporting the hypothesis that IL-6 and iNOS /NO release after LPS stimulation is mediated by TNF-α rather than LPS directly. The pharmacodynamic model allowed a well understanding of the cellular pharmacodynamic mechanism of baicalein in the treatment of inflammatory diseases.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 14%
Researcher 2 14%
Student > Bachelor 1 7%
Other 1 7%
Student > Postgraduate 1 7%
Other 0 0%
Unknown 7 50%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 4 29%
Biochemistry, Genetics and Molecular Biology 1 7%
Agricultural and Biological Sciences 1 7%
Medicine and Dentistry 1 7%
Unknown 7 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 August 2018.
All research outputs
#20,529,173
of 23,098,660 outputs
Outputs from Frontiers in Pharmacology
#10,328
of 16,458 outputs
Outputs of similar age
#287,709
of 329,176 outputs
Outputs of similar age from Frontiers in Pharmacology
#275
of 407 outputs
Altmetric has tracked 23,098,660 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 16,458 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 407 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.