ArticleJournal of natural medicines2016
Informatics framework of traditional Sino-Japanese medicine (Kampo) unveiled by factor analysis.
Article in Journal of natural medicines, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 8 citations in OpenAlex.
- Cheminformatics modeling of the correlation between Bupleurum Root-formula medicines and Excess and Deficiency pattern in the diagnostic criteria of Sho in Kampo (traditional Japanese medicine) by non-targeted direct infusion mass spectrometry with machine learning.Journal of natural medicines · 2022Article
- 5,7-Dihydroxyflavone Analogues May Regulate Lipopolysaccharide-Induced Inflammatory Responses by Suppressing IEvidence-based complementary and alternative medicine : eCAM · 2017Article
- Finding an appropriate equation to measure similarity between binary vectors: case studies on Indonesian and Japanese herbal medicines.BMC bioinformatics · 2016Article
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Authors and funding
10 authors at 4 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Kampo, an empirically validated system of traditional Sino-Japanese medicine, aims to treat patients holistically. This is in contrast to modern medicine, which focuses in principle on treating the affected parts of the body of the patient. Kampo medicines formulated as combinations of crude drugs are prescribed based on a Kampo-specific diagnosis called Sho (in Japanese), defined as the holistic condition of each patient. Therefore, the medication system is very complex and is not well understood from a modern scientific perspective. Here, we show the informatics framework of Kampo medication by multivariate factor analysis of the elements constituting Kampo medication. First, the variation of Kampo formulas projected by principal component analysis (PCA) indicated that the combination patterns of crude drugs were highly correlated with Sho diagnoses of Deficiency and Excess. In an opposite way, partial least squares projection to latent structures (PLS) regression analysis could also predict Deficiency/Excess only from the composed crude drugs. Secondly, to chemically verify the correlation between Deficiency/Excess and crude drugs, we performed mass spectrometry (MS)-based metabolome analysis of Kampo prescriptions. PCA and PLS regression analysis of the metabolome data also suggested that Deficiency/Excess could be theoretically explained based on the variation in chemical fingerprints of Kampo medicines. Our results show that factor analysis of Kampo concepts and of the metabolomes of Kampo medicines enables interpretation of the complex system of Kampo. This study will theoretically form the basis for establishing traditionally and empirically based medications worldwide, leading to systematically personalized medicine.
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