ABSTRACT
Introduction: Medicine innovation is crucial in promoting the sustainable development of medicine undertakings, which has significant economic and social benefits. China is the main force in global medicine consumption, with a huge demand for innovative medicines. Thus, the Chinese government releases a series of policies aimed at providing scientific and reasonable guidance for medicine innovation. However, there is inadequate quantitative evaluation and comparison of various medicine innovation policies in the existing studies. Methods: This paper adopts the approach of text mining and the Policy Modeling Consistency Index (PMC-Index) model to construct an evaluation system and then quantitatively evaluates and compares the traditional Chinese medicine innovation policies (TCMIPs), the biological medicine innovation policies (BMIPs), and the multiple medicine innovation policies (MMIPs) in China. Results: The results indicate that: (1) The three types of drug innovation policies have similarities in content and goal through comparative analysis of high-frequency words, while they also have their own characteristics. (2) The average PMC-Index of 29 TCMIPs is 5.77, which has the highest policy bad rate (21%); the average PMC-Index of 12 BMIPs is 6.21, which has the highest policy good rate (92%); moreover, the average PMC-Index of 35 MMIPs is 6.06, which has the highest policy excellence rate (26%). (3) The BMIPs, MMIPs, and TCMIPs have similar scores on policy object, policy orientation, policy timeliness, policy evaluation, and policy accessibility, while they differ significantly mainly on policy nature, incentive method, policy function, policy issuing agency, and policy instrument. Discussion: This study contributes to a comprehensive understanding of medicine innovation policies in China, in order to provide theoretical support for future policy formulation and optimization in the medicine industry. Moreover, we expand the application scenarios of policy diffusion theory.
Subject(s)
Health Policy , Medicine, Chinese Traditional , China , Humans , Data Mining , InventionsABSTRACT
Polysaccharide decolorization has a major effect on polysaccharide function. In the present study, the decolorization of Rehmannia glutinosa polysaccharides (RGP) is optimized using two methods-the AB-8 macroporous resin (RGP-1) method and the H2O2 (RGP-2) method. The optimal decolorization parameters for the AB-8 macroporous resin method were as follows: temperature, 50 °C; macroporous resin addition, 8.4%; decolorization duration, 64 min; and pH, 5. Under these conditions, the overall score was 65.29 ± 3.4%. The optimal decolorization conditions for the H2O2 method were as follows: temperature, 51 °C; H2O2 addition, 9.5%; decolorization duration, 2 h; and pH, 8.6. Under these conditions, the overall score was 79.29 ± 4.8%. Two pure polysaccharides (RGP-1-A and RGP-2-A) were isolated from RGP-1 and RGP-2. Subsequently, their antioxidant and anti-inflammatory effects and mechanisms were evaluated. RGP treatment activated the Nrf2/Keap1 pathway and significantly increased the activity of antioxidant enzymes (p < 0.05). It also inhibited the expression of pro-inflammatory factors and suppressed the TLR4/NF-κB pathway (p < 0.05). RGP-1-A had a significantly better protective effect than RGP-2-A, likely owing to the sulfate and uronic groups it contains. Together, the findings indicate that RGP can act as a natural agent for the prevention of oxidation and inflammation-related diseases.
ABSTRACT
INTRODUCTION: The methylation at position N6 of adenine is called N6-methyladenosine (m6A). This transcriptional RNA modification exerts a very active and important role in RNA metabolism and in other biological processes. However, the activities of m6A associated with malignant liver hepatocellular carcinoma (LIHC) are unknown and are worthy of study. MATERIALS AND METHODS: Using the data of University of California, Santa Cruz (UCSC), the expression of M6A methylation regulators in pan-cancer was evaluated as a screening approach to identify the association of M6A gene expression and 18 cancer types, with a specific focus on LIHC. LIHC datasets of The Cancer Genome Atlas (TCGA) were used to explore the expression of M6A methylation regulators and their clinical significance. Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were used to explore the underlying mechanism based on the evaluation of aberrant expression of m6A methylation regulators. RESULTS: The expression alterations of m6A-related genes varied across cancer types. In LIHC, we found that in univariate Cox regression analysis, up-regulated m6A modification regulators were associated with worse prognosis, except for ZC3H13. Kaplan-Meier survival curve analysis indicated that higher expression of methyltransferase-like protein 3 (METTL3) and YTH N6-methyladenosine RNA binding protein 1 (YTHDF1) genes related to the worse survival rate defined by disease-related survival (DSS), overall survival (OS), progression-free interval (PFI), and disease-free interval (DFI). Up-regulated m6A methylation regulator group (cluster2) obtained by consensus clustering was associated with poor prognosis. A six-gene prognostic signature established using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm performed better in the early (I + II; T1 + T2) stages than in the late (III + IV; T3 + T4) stages of LIHC. Using the gene signature, we constructed a risk score and found that it was an independent predictive factor for prognosis. Using GSEA, we identified processes involved in DNA damage repair and several biological processes associated with malignant tumors that were closely related to the high-risk group. CONCLUSION: In summary, our study identified several genes associated with m6A in LIHC, especially METTL3 and YTHDF1, and confirmed that a risk signature comprised of m6A-related genes was able to forecast prognosis.
