Compatible stability study of XingNaoJing injection based on physical-chemical properties analysis
TMR Modern Herbal Medicine. 2019, 2 (4): 173-182.
Objective: The clinical treatment of brain diseases is urgent. Xingnaojing (XNJ) injection is often used in combination with other injection drugs. Due to the possible interaction between the injections in vivo, the particle size, osmotic pressure, pH value change and component stability decrease, that is one of the important factors causing various adverse reactions. Based on the above situation, this study investigated the physical properties and chemical composition changes of XNJ injection and its compatibility solvent and 13 kinds of clinical injection, speculated the possible interactions between the drugs in vivo from the perspective of in vitro compatibility stability, find out the safety risks of adverse reactions and provide guidance for the safe and rational use of XNJ injection. Methods: According to the clinical application, XNJ injection was mixed with 13 combination injections based on 250 mL 5% glucose injection, and placed at room temperature for 6 h. Then, the clarity, particle size, pH, osmolality, and the contents of camphor, d-borneol, and muscone of the compatible solutions were detected at 0, 1, 2, 4, and 6 h, respectively. Results: The results showed that the physical-chemical properties of compatibility solution were slightly influenced when XNJ was combined with Alprostadil injection and Danhong injection. The change of particle size and the degradation of muscone content were the main factors affecting the compatibility stability of XNJ injection, indicating that there are some problems in compatibility stability, which may be one of the causes of clinical adverse reactions. Conclusion: This study suggests that XNJ injection in combination with other injections during intravenous administration should be performed cautiously.
Similarity measurement of Chinese medicine ingredients
for cold-hot nature identification
TMR Modern Herbal Medicine. 2019, 2 (4): 183-191.
Objective: Nature theory of Chinese medicine (CM) is the core basic theory of
Traditional Chinese Medicine (TCM), in which cold-hot nature is the focus of
research. Studies have found that CM ingredients are the material basis for the
production of medicine natures. Therefore, it is speculated that CMs with
similar composition of substances should have similar medicinal nature. Modern work
studies cold-hot medicine of CMs with chemical fingerprinting technology because
the chemical fingerprint data of CM can reflect the whole composition of CM ingredients. Methods: To verify the hypothesis
above, in this work, we study quantifying the similarity of CM ingredients to
fingerprint similarity, and explore the relationship between the composition of
CMs and cold-hot nature. Firstly, we utilize ultraviolet (UV) spectrum technology to analyze 61 CMs, which
have clear cold-hot nature (including 30 ‘cold’ CMs and 31 ‘hot’ CMs).
Secondly, with the constructed fingerprint database of CMs, a distance metric
learning algorithm is studied to metric the similarity of UV fingerprints. Finally, a retrieval scheme is proposed to build a predictive
identification model to identify cold-hot nature of CMs. Results: By means of numerous experiment analyses, ultraviolet
spectrum data of petroleum ether solvent can better represent CMs to distinguish between cold and hot natures. Comparing
with existing classical models, the proposed identification scheme has better
predictive performance. Conclusion: The experimental results
prove our inference that CMs with similar
composition of substances should have similar medicinal nature. The proposed prediction model is proved to be effective and feasible.
Identifi cation of prognostic markers by integrating the genome and transcriptomics in ovarian cancer
TMR Modern Herbal Medicine. 2019, 2 (4): 192-205.
order to find a genetic marker to predict the prognosis of patients with
ovarian cancer based on multi-omics data.
Methods: We download RNA-Seq
SNP, CNV data and clinical follow-up information from TCGA database and
randomly divide them into training set and test set. GSE17260 dataset in GEO is
taken as an external validation set. Prognosis-related genes, copy number
difference genes and mutant genes are screened in the training set. After the
integration of genes, the random forest algorithm is further used for feature
selection, ultimately obtaining a robust biomarker. On this basis, a
gene-related prognostic model is established and verified in the test set and
verification set. Results: We have obtained 2097 prognostic related
genes, 447 copy amplification genes, 1069 copy deletion genes and 654
significant mutations genes. Through the feature selection of random forest
algorithm, five feature genes (PSMB1, COL6A6, SLC22A2, KLHL23 and CD3G) are
obtained by integrating these genes, some of which have been reported to be
related to tumor progress. Furthermore, the prognostic risk assessment model of
5-gene signature is established by Cox regression analysis. The model can
evaluate the risk of patient samples in training set, test set and external
verification set. 5-gene signature shows strong robustness and clinical
independence. The results of GSEA analysis also show that the pathway of 5-gene
signature enrichment is significantly related to the pathway and biological
process of the occurrence and development of ovarian cancer. Conclusion: In
this study, 5-gene signature is constructed as a new prognostic marker to
predict the survival of patients with ovarian cancer.
Bio-characteristic profiling related to clinic: A new technology platform for quality evaluation of Chinese materia medic
TMR Modern Herbal Medicine. 2019, 2 (4): 215-224.
Quality evaluation is a bottleneck restricting the modernization and internationalization of Chinese materia medica (CMM). Due to characteristics of multi-component, multi-efficacy, multi-target, the existing quality evaluation system still cannot fully meet quality control needs of CMM. Hence, the author put forward “Bio-characteristic profiling related to clinic, BPRC” academic concept, meanwhile, fully take the advantage of analysis method with clinical monitoring superiority or profiling characteristics, build up "BPRC" new technology platform, in order to realize “real-time, dynamic and full-range monitoring” new technologies of CMM quality evaluation system and promote the further development of CMM industry.
Networ k pharmacology based method for mechanistic investigation of the Compound Xintahua in the treatment of atherosclerosis
TMR Modern Herbal Medicine. 2019, 2 (4): 225-236.
Objective: To explore the pharmacological basis of the Compound Xintahua (XTH) action in Atherosclerosis (AS) therapy, a network interaction analysis was conducted at the molecular level. Methods: TCMSP database and literature mining were used to analyze the main effective components in XTH, and the targets were predicted by Swiss Target Prediction server according to AS mechanism. The potential targets were introduced into the FunRich database for target annotation and analysis, the path analysis was finally performed based on the FunRich databases. To determine the mechanism of action of XTH. Results: A total of 316 compounds, 117 targets, and 290 signaling pathways were identified. And 16 effective compounds, 39 common targets, and 43 pathways were associated with AS. Conclusions: The results showed that the flavonoids, phenols, organic acids and terpenoids of XTH could participate in the process of lipid metabolism, angiogenesis, oxidation, inflammation, endocrine metabolism, cell proliferation and apoptosis, It was further found that they could play the role of anti-Atherosclerosis through multi-component, multi-target, and multi-channel synergistically.