2019
DOI: 10.3390/molecules24234254
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The Effects of Different Varieties of Aurantii Fructus Immaturus on the Potential Toxicity of Zhi-Zi-Hou-Po Decoction Based on Spectrum-Toxicity Correlation Analysis

Abstract: In accordance with the provision in China Pharmacopoeia, Citrus aurantium L. (Sour orange—SZS) and Citrus sinensis Osbeck (Sweet orange—TZS) are all in line with the requirements of Aurantii Fructus Immaturus (ZS). Both kinds of ZS are also marketed in the market. With the frequent occurrence of depression, Zhi-Zi-Hou-Po decoction (ZZHPD) has attracted wide attention. Currently, studies have shown that ZZHPD has a potential toxicity risk, but the effect of two commercial varieties of ZS on ZZHPD has not been r… Show more

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Cited by 14 publications
(9 citation statements)
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“…With the increase of the RSBDP dose, colon tissue damage did not decrease but increased ( Figure 8 ). This may be related to the accumulation of the toxicity of traditional Chinese medicine ( Zhang and Feng, 2019 ).…”
Section: Resultsmentioning
confidence: 99%
“…With the increase of the RSBDP dose, colon tissue damage did not decrease but increased ( Figure 8 ). This may be related to the accumulation of the toxicity of traditional Chinese medicine ( Zhang and Feng, 2019 ).…”
Section: Resultsmentioning
confidence: 99%
“…The suitability of the PCA model was assessed by cross-validation before analysis, and no external validation (additional freeze-dried mannitol-HPMC excipients) was carried out. OPLS-DA has been widely used in the identification of the chemical markers of traditional Chinese medicine and its compound prescriptions, as well as the identification of biomarkers in metabolomics in recent years. Validation of an analysis model is critical for multivariate statistical analysis. It usually requires an external validation with a training data set and test data set, but in cases with few actual samples and large statistical data, external validation may not be demanded.…”
Section: Results and Discussionmentioning
confidence: 99%
“…The parameters are then gradient updated according to the stochastic gradient descent algorithm. After continuous learning to make the final loss meet the accuracy requirements, the parameter values of each layer of the network can be retained without reinitialization the next time the network is used [ 19 ].…”
Section: Experimental Materials and Methodsmentioning
confidence: 99%