Inviting an author to review:
Find an author and click ‘Invite to review selected article’ near their name.
Search for authorsSearch for similar articles
1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      A pharmacokinetic-pharmacodynamic study to elucidate the cardiovascular protective constituents in Danhong Injection

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Related collections

          Most cited references33

          • Record: found
          • Abstract: found
          • Article: not found

          Correlation Coefficients

          Correlation in the broadest sense is a measure of an association between variables. In correlated data, the change in the magnitude of 1 variable is associated with a change in the magnitude of another variable, either in the same (positive correlation) or in the opposite (negative correlation) direction. Most often, the term correlation is used in the context of a linear relationship between 2 continuous variables and expressed as Pearson product-moment correlation. The Pearson correlation coefficient is typically used for jointly normally distributed data (data that follow a bivariate normal distribution). For nonnormally distributed continuous data, for ordinal data, or for data with relevant outliers, a Spearman rank correlation can be used as a measure of a monotonic association. Both correlation coefficients are scaled such that they range from -1 to +1, where 0 indicates that there is no linear or monotonic association, and the relationship gets stronger and ultimately approaches a straight line (Pearson correlation) or a constantly increasing or decreasing curve (Spearman correlation) as the coefficient approaches an absolute value of 1. Hypothesis tests and confidence intervals can be used to address the statistical significance of the results and to estimate the strength of the relationship in the population from which the data were sampled. The aim of this tutorial is to guide researchers and clinicians in the appropriate use and interpretation of correlation coefficients.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Danhong injection in cardiovascular and cerebrovascular diseases: Pharmacological actions, molecular mechanisms, and therapeutic potential

            Cardiovascular and cerebrovascular diseases are the main cause of mortality worldwide, currently with less than optimum therapeutic options. Danhong injection (DHI) is a medicinal preparation based on two eminent Chinese herbal medicines, Salviae Miltiorrhizae (Dan Shen; family: Lamiaceae) and Flos Carthami (Hong Hua; family: Compositae/Asteraceae). DHI has been mainly used in the clinical therapy of cardiovascular (such as acute coronary syndrome and angina pectoris) and cerebrovascular diseases (such as stroke) in China for many years. The pharmacological properties of DHI include anti-inflammatory, anti-oxidant, anti-coagulatory, hypolipidemic, anti-apoptotic, vasodilatory, and angiogenesis-promoting actions. DHI offers a safe and effective therapeutic agent against cardiovascular and cerebrovascular diseases by modulating multiple disease-relevant signaling pathways and molecular targets. Herein, we provide a comprehensive review of the phytochemistry, therapeutic effects, molecular mechanisms, and adverse reactions of DHI in cardiovascular and cerebrovascular diseases. We also highlight the latest pharmacological advances and therapeutic potential of this promising herb-derived cardiovascular drug preparation.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              A comprehensive review of integrative pharmacology-based investigation: A paradigm shift in traditional Chinese medicine

              Over the past decade, traditional Chinese medicine (TCM) has widely embraced systems biology and its various data integration approaches to promote its modernization. Thus, integrative pharmacology-based traditional Chinese medicine (TCMIP) was proposed as a paradigm shift in TCM. This review focuses on the presentation of this novel concept and the main research contents, methodologies and applications of TCMIP. First, TCMIP is an interdisciplinary science that can establish qualitative and quantitative pharmacokinetics–pharmacodynamics (PK–PD) correlations through the integration of knowledge from multiple disciplines and techniques and from different PK–PD processes in vivo. Then, the main research contents of TCMIP are introduced as follows: chemical and ADME/PK profiles of TCM formulas; confirming the three forms of active substances and the three action modes; establishing the qualitative PK–PD correlation; and building the quantitative PK–PD correlations, etc. After that, we summarize the existing data resources, computational models and experimental methods of TCMIP and highlight the urgent establishment of mathematical modeling and experimental methods. Finally, we further discuss the applications of TCMIP for the improvement of TCM quality control, clarification of the molecular mechanisms underlying the actions of TCMs and discovery of potential new drugs, especially TCM-related combination drug discovery.
                Bookmark

                Author and article information

                Journal
                Journal of Pharmaceutical and Biomedical Analysis
                Journal of Pharmaceutical and Biomedical Analysis
                Elsevier BV
                07317085
                September 2022
                September 2022
                : 219
                : 114953
                Article
                10.1016/j.jpba.2022.114953
                35901531
                19e92498-c4d8-4756-b1e9-df00adf2d6c3
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

                History

                Comments

                Comment on this article