Journal of Traditional Chinese Medicine ›› 2024, Vol. 44 ›› Issue (4): 713-721.DOI: 10.19852/j.cnki.jtcm.20240521.001
• Original articles • Previous Articles Next Articles
SONG Zhenguang1,2, YANG Bin3, WANG Fei1, YAN Dongmei1, ZHOU Xiaoqing1, HUANG Liping1, GAO Xuemei1, LI Bin1(), HUANG Luqi3(
)
Received:
2023-11-22
Accepted:
2024-03-11
Online:
2024-08-15
Published:
2024-05-21
Contact:
Prof LI Bin, Academician Workstation, Jiangxi University of Chinese Medicine, Nanchang 330004, China. Supported by:
SONG Zhenguang, YANG Bin, WANG Fei, YAN Dongmei, ZHOU Xiaoqing, HUANG Liping, GAO Xuemei, LI Bin, HUANG Luqi. Study on the four Qi of Pfaffia glomerata based on the metabolomics technology and comparison of Dangshen (Radix Codonopsis) in the equivalent substitution prescription[J]. Journal of Traditional Chinese Medicine, 2024, 44(4): 713-721.
Figure 1 Stability heat map of urine QC samples A: stability heat map of QC in positive ion mode; B: stability heat map of QC in negative ion mode. QC: quality control; ESI: electrospray ionization; +: positive ion mode; -: negative ion mode; R2: coefficient of determination.
Figure 2 Establishment of the prediction model for cold-hot medicinal properties in positive ion mode A: the principal components analysis score plots of cold herb and hot herb group in positive ion mode. principal components analysis-X test for intra-group differences, blue dots are cold herbs, green dots are hot herbs. B: the best training set for distinguishing properties of cold-hot herbs in OPLS-DA model, blue dots are cold herbs, green dots are hot herbs. C: the Positive Ion OPLS-DA Model Permutation Test Diagram. OPLS-DA: Orthogonal Partial Least Squares-Discriminant Analysis.
Figure 3 Establishment of the prediction model for cold-hot medicinal properties in negative ion mode A: the principal components analysis score plots of cold herbs and hot herbs group in negative ion mode. principal components analysis-X test for intra-group differences, blue dots are cold herbs, green dots are hot herbs. B: the best training set for distinguishing properties of cold-hot herbs in OPLS-DA model under negative mode, the blue dots are cold herbs, and the green dots are hot herbs. C: the negative ion OPLS-DA model permutation test diagram. OPLS-DA: Orthogonal Partial Least Squares-Discriminant Analysis.
Figure 4 The distribution of PGPG in cold-hot discrimination model A: the distribution of PGPG in cold-hot discrimination model under positive ion mode, the green dots are hot herb, the blue dots are cold herb, the red dots are PGPG. B: the distribution of PGPG in cold-hot discrimination model under negative ion mode, the green dots are hot herb, the blue dots are cold herb, the red dots are PGPG. PGPG: Pfaffia glomerata prescription group.
Item | Samples | 1 | 2 | Accuracy (%) |
---|---|---|---|---|
Hot herbs | 35 | 31 | 4 | 88.6 |
Cold herbs | 38 | 7 | 31 | 81.6 |
CPPG | 9 | 1 | 8 | 88.9 |
PGPG | 9 | 1 | 8 | 88.9 |
Table 1 Prediction results of cold-hot medicine properties of PG prescription in the positive ion mode
Item | Samples | 1 | 2 | Accuracy (%) |
---|---|---|---|---|
Hot herbs | 35 | 31 | 4 | 88.6 |
Cold herbs | 38 | 7 | 31 | 81.6 |
CPPG | 9 | 1 | 8 | 88.9 |
PGPG | 9 | 1 | 8 | 88.9 |
Item | Samples | 1 | 2 | 3 | Accuracy (%) |
---|---|---|---|---|---|
Hot herbs | 35 | 34 | 4 | 0 | 97.14 |
Cold herbs | 38 | 7 | 31 | 0 | 81.58 |
CPPG | 9 | 1 | 0 | 8 | 88.89 |
PGPG | 9 | 1 | 0 | 8 | 88.89 |
Table 2 Comparison of PG prescription and CP prescription in cold-hot medicine property distribution area under positive ion mode
Item | Samples | 1 | 2 | 3 | Accuracy (%) |
---|---|---|---|---|---|
Hot herbs | 35 | 34 | 4 | 0 | 97.14 |
Cold herbs | 38 | 7 | 31 | 0 | 81.58 |
CPPG | 9 | 1 | 0 | 8 | 88.89 |
PGPG | 9 | 1 | 0 | 8 | 88.89 |
Item | Members | 1 | 2 | 3 | Accuracy (%) |
---|---|---|---|---|---|
Cool herbs | 43 | 42 | 1 | 0 | 97.7 |
Cold herbs | 38 | 5 | 33 | 0 | 86.8 |
CPPG | 9 | 8 | 1 | 0 | 88.9 |
PGPG | 9 | 7 | 2 | 0 | 77.8 |
Table 3 The prediction results of cold-cool medicine properties of PGPG and CPPG in the positive ion mode
Item | Members | 1 | 2 | 3 | Accuracy (%) |
---|---|---|---|---|---|
Cool herbs | 43 | 42 | 1 | 0 | 97.7 |
Cold herbs | 38 | 5 | 33 | 0 | 86.8 |
CPPG | 9 | 8 | 1 | 0 | 88.9 |
PGPG | 9 | 7 | 2 | 0 | 77.8 |
M/Z | RT (Min) | HMDB number | Name | Cold medicine group (n = 5) | Cool medicine group (n = 5) | PGPG |
---|---|---|---|---|---|---|
330.056 | 3.06 | HMDB0000058 | Cyclic AMP | ↓a | ↓a | ↓ |
311.157 | 5.96 | HMDB0029113 | Tyrosyl-Proline | ↑b | ↑a | ↑ |
302.135 | 12.5 | HMDB0030717 | (R)-3',7-Dihydroxy-2',4'-dimethoxyisoflavan | ↓a | ↓b | ↓b |
384.113 | 5.04 | HMDB0014390 | Benzatropine | ↓a | ↓b | ↓ |
119.076 | 16.26 | HMDB0030647 | Artemidinol | ↑a | ↑b | ↑b |
670.41 | 15.06 | HMDB0014602 | Acitretin | ↓a | ↓b | ↑ |
419.245 | 17.24 | HMDB0041809 | 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanol | ↓b | ↓a | ↓a |
705.363 | 15.71 | HMDB0030389 | Quebrachidine | ↓a | ↓a | ↓ |
518.31 | 15.61 | HMDB0012501 | 10,11-Dihydro-12R-hydroxy-leukotriene E4 | ↓a | ↓a | ↓ |
Table 4 Change trend of biomarkers in cold herbs group, cool herbs group and PGPG
M/Z | RT (Min) | HMDB number | Name | Cold medicine group (n = 5) | Cool medicine group (n = 5) | PGPG |
---|---|---|---|---|---|---|
330.056 | 3.06 | HMDB0000058 | Cyclic AMP | ↓a | ↓a | ↓ |
311.157 | 5.96 | HMDB0029113 | Tyrosyl-Proline | ↑b | ↑a | ↑ |
302.135 | 12.5 | HMDB0030717 | (R)-3',7-Dihydroxy-2',4'-dimethoxyisoflavan | ↓a | ↓b | ↓b |
384.113 | 5.04 | HMDB0014390 | Benzatropine | ↓a | ↓b | ↓ |
119.076 | 16.26 | HMDB0030647 | Artemidinol | ↑a | ↑b | ↑b |
670.41 | 15.06 | HMDB0014602 | Acitretin | ↓a | ↓b | ↑ |
419.245 | 17.24 | HMDB0041809 | 4-(Methylnitrosamino)-1-(3-pyridyl)-1-butanol | ↓b | ↓a | ↓a |
705.363 | 15.71 | HMDB0030389 | Quebrachidine | ↓a | ↓a | ↓ |
518.31 | 15.61 | HMDB0012501 | 10,11-Dihydro-12R-hydroxy-leukotriene E4 | ↓a | ↓a | ↓ |
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