Abdominal examination using pressure pain threshold algometer reflecting clinical characteristics of complementary and alternative medicine in Korea: A systematic review and a brief proposal.

BACKGROUND: A review was conducted to determine a pressure algometry measurement during abdominal examination that reflects clinical settings of traditional Korean medicine. METHODS: After reviewing the previous studies, we have proposed a reference index, common posture and method for performing pressure algometry during abdominal examination. Keyword search using eight databases was performed. To identify the characteristics of pressure algometry during abdominal examination, keywords (e.g., abdomen, abdominal examination) were searched in national Korean databases, including Korean studies Information System (KISS), Research Information Sharing Service (RISS), and Oriental Medicine Advanced Searching Integrated System (OASIS). To examine the methods of measuring pressure pain threshold (PPT) with validity and reliability, combination keywords (e.g., PPT, pressure algometry, pressure pain, validity, reliability) were searched in Pubmed, Cochrane library, Google scholar, Ovid Embase, and China Knowledge Resource Integrated Database (CNKI). RESULTS: A total of 652 articles were identified, and 22 relevant articles were included. The following main indices are proposed as a standardized pressure algometry method during abdominal examination: unit of measurements, measuring tools, target locations, pressure area, pressure rate, posture of patients, and evaluators. Based on the results of the review combined with clinical practice, useful indices for pressure algometer during abdominal examination were derived (target location: CV 12, unit of measurement: MPa, pressure area: 4 cm2, pressure rate: 0.098 MPa/s, posture of patient: supine position, number, and intervals of measurement: three consecutive measurements at intervals of 30 seconds, 5 minutes rest prior to commencement). Postures and method sequence of pressure algometry during abdominal examination are also proposed. CONCLUSIONS: Using standardized indices, postures and method for abdominal examination in clinical settings will help make objective assessments.

Current status of herbal medicine research for respiratory diseases induced by particulate matter: a protocol for a scoping review.

BACKGROUND: Particulate matter (PM) is an important environmental risk factor for the initiation and exacerbation of respiratory disease. Various herbal medicines have exhibited a reduction in symptoms of respiratory diseases induced by PM in animal models. However, the types and characteristics of studies on herbal medicine for respiratory diseases by PM have not been reviewed. This scoping review will focus on the currents status and research gap of herbal medicines for respiratory diseases caused by PM. METHODS: We will follow the scoping review framework developed by Arksey and O'Malley. MEDLINE (via PubMed), EMBASE, and the Cochrane Central Register of Controlled Trials will be searched for relevant English-language publications, and only peer-reviewed, controlled comparative in vivo/in-vitro/human studies examining the effects of herbs on respiratory disease induced by PM will be included. The basic characteristics, research methods, detailed regimens, possible mechanisms, outcomes, and results will be extracted using a predefined standardized data extraction form. Outcomes will be presented in the following categories: pulmonary function, inflammatory markers, reactive oxygen species, histology and mechanisms, and adverse events. Two researchers will independently perform the study selection, data extraction, and quality assessment. We will also present the research map and implications for further study. ETHICS AND DISSEMINATION: Ethical approval is not required because individual patient data will not be included. The findings will be disseminated through peer-reviewed publications or conference presentations. SYSTEMATIC REVIEW REGISTRATION: This review protocol has been registered with the Open Science Framework on February 12, 2021 ( https://osf.io/s7uvk/ ).

Exploring the combination and modular characteristics of herbs for alopecia treatment in traditional Chinese medicine: an association rule mining and network analysis study.

BACKGROUND: Although alopecia affects the quality of life, its pathogenesis is unknown, because cellular interactions in the hair follicle are complex. Several authors have suggested using herbal medicine to treat alopecia, and bioinformatics and network pharmacology may constitute a new research strategy in this regard because herbal medicines contain various chemical components. This study used association rule mining (ARM) and network analysis to analyze the combinations of medicinal herbs used to treat alopecia. METHODS: We searched Chinese, Korean, and English databases for literature about alopecia treatment, extracting the names of each herbal prescription and herb. The meridian tropism and classification category of each herb were also investigated. Using ARM, we identified frequently combined two-herb and three-herb sets. Using network analysis, we divided the herbs into several modules according to prescription pattern. RESULTS: Fifty-six articles and 489 herbal medicines were included-312 internal and 177 external medicines. Among the 312 medicinal herbs used in internal medicine group, the most frequently combined two-herb set was Polygonum multiflorum Thunb. () and Angelica sinensis (Oliv.) Dlels (). The most frequently used three-herb combination was Polygonum multiflorum Thunb., Angelica sinensis (Oliv.) Dlels, and Ligusticum chuanxiong Hort. (). In network analysis, three modules were identified. The herbs of Module 1 were related to the liver and kidney meridians, and those of Module 3 were related to the Stomach meridian. CONCLUSIONS: We identified the frequency, characteristics, and functional modules of herb combinations frequently used in alopecia treatment. We confirmed the value of classical medicinal herb theory. This finding will prompt further bioinformatics and network pharmacology research on alopecia.