Designing clinical studies that take into account traditional East Asian medicine's systems and methods - with focus on pattern identification

Revealing Associations between Diagnosis Patterns and Acupoint Prescriptions Using Medical Data Extracted from Case Reports

OBJECTIVE

The optimal acupoints for a particular disease can be determined by analysis of diagnosis patterns. The objective of this study was to reveal the association between such patterns and the acupoints prescribed in clinical practice using medical data extracted from case reports.

METHODS

This study evaluated online virtual diagnoses made by currently practicing Korean medical doctors (N = 80). The doctors were presented with 10 case reports published in Korean medical journals and were asked to diagnose the patients and prescribe acupoints accordingly. A network analysis and the term frequency-inverse document frequency (tf-idf) method were used to analyse and quantify the relationship between diagnosis patterns and prescribed acupoints.

RESULTS

The network analysis showed that ST36, LI4, LR3, and SP6 were the most frequently used acupoints across all diagnoses. The tf-idf values showed the acupoints used for specific diseases, such as BL40 for bladder disease and LU9 for lung disease.

CONCLUSIONS

The associations between diagnosis patterns and prescribed acupoints were identified using an online virtual diagnosis modality. Network and text mining analyses revealed commonly applied and disease-specific acupoints in both qualitative and quantitative terms.

Pathway of PPAR-gamma coactivators in thermogenesis: a pivotal traditional Chinese medicine-associated target for individualized treatment of rheumatoid arthritis

Traditional Chinese medicine (TCM) syndromes have been regarded as the crucial clinical manifestations for individualized diagnosis and treatment of complex diseases, including rheumatoid arthritis (RA) and cancer. Especially, RA patients are classified into cold and hot syndromes with different clinical manifestations, interventions and molecular mechanisms. Better effectiveness of a classic cold syndrome-specific herbal formula Wu-tou decoction (WTD) has been achieved. To explore molecular mechanisms of syndrome-specific formulae is of great clinical significance to improve the effectiveness and pertinence of treatment for the complex diseases with personalized conditions. However, the scientific basis of WTD treatment on RA with the cold syndrome remains unclear. Here, we predicted the putative targets for composite compounds contained in WTD using drugCIPHER-CS and constructed a WTD herbs-putative targets-RA related genes network. Next, a list of major WTD targets was identified based on their topological features, including the degree, node betweenness, closeness and k-coreness in the above pharmacological network. Importantly, pathway enrichment analysis revealed that these major WTD targets were significantly associated with the pathway of peroxisome proliferator-activated receptor (PPAR)-gamma (PPAR-γ) coactivators in thermogenesis. These computational findings were subsequently verified by experiments on a rat model of collagen-induced arthritis (CIA) with cold or hot syndromes, and on human fibroblast-like synoviocytes-rheumatoid arthritis (HFLS-RA) cell line. In conclusion, the pathway of PPAR-γ coactivators in thermogenesis might be one of the potential pharmacological targets of WTD to alleviate RA with the TCM cold syndrome. These findings may open new avenues for designing individualized treatment regimens for RA patients.

Incorporating pattern identification of Chinese medicine into precision medicine: An integrative model for individualized medicine

On 20 January, 2015, U.S. President Obama announced an ambitious plan called "Precision Medicine (PM) Initiative", aiming to deliver genetics-based medical treatments. PM has shown a promising prospect by tailoring disease treatments and preventions to individuals. However, a predominantly genetics-based method restricts its benefit and applicability in most chronic and complex diseases. Pattern identification (PI) is one of the representative characteristics of Chinese medicine implying the concept of holism and individualized treatment. It is another classification method taking environmental, psychosocial and other individual factors into account. Integrating PI with disease diagnosis of Western medicine will provide a strong complement to genetics-based PM, thus establish an integrative model for individualized medicine. PI provides new perspectives for PM, not only in clinical practice, but also in new drug development and clinical trial design. It is for sure that the integrative approach will ultimately lead to a safer, more convenient and effective patient-centered healthcare and most patients will benefit in the era of PM.

Data Mining of Acupoint Characteristics from the Classical Medical Text: DongUiBoGam of Korean Medicine

Throughout the history of East Asian medicine, different kinds of acupuncture treatment experiences have been accumulated in classical medical texts. Reexamining knowledge from classical medical texts is expected to provide meaningful information that could be utilized in current medical practices. In this study, we used data mining methods to analyze the association between acupoints and patterns of disorder with the classical medical book DongUiBoGam of Korean medicine. Using the term frequency-inverse document frequency (tf-idf) method, we quantified the significance of acupoints to its targeting patterns and, conversely, the significance of patterns to acupoints. Through these processes, we extracted characteristics of each acupoint based on its treating patterns. We also drew practical information for selecting acupoints on certain patterns according to their association. Data analysis on DongUiBoGam's acupuncture treatment gave us an insight into the main idea of DongUiBoGam. We strongly believe that our approach can provide a novel understanding of unknown characteristics of acupoint and pattern identification from the classical medical text using data mining methods.