Procoagulant Properties of the Combined Extracts of Zingiber officinale and Ageratina adenophora in Renal Tests

Bryan C.  O’Brien   |  Jaypo F. Omanito  |  Melchor D. Tomolnoc
School of  Natural Sciences
Ms. Erlinda Sanchez, Adviser

Abstract

This study evaluated the procoagulant properties of the combined extracts of Zingiber officinale and Ageratina adenophora in varying ratios (0.50:0.50, 0.75:0.25, and 0.25:0.75) and its effects on levels of Blood Urea Nitrogen and Serum Creatinine.  The parallel group design was utilized in investigating the procoagulant properties of the combined extracts of Zingiber officinale and Ageratina adenophora in relation to human venous blood compared to the effect of the clot activator in the SST on the time of clotting. The combined extracts resulted to relatively shorter time of clotting than the tubes with Serum Separator Tubes (SST). The combined extracts with a concentration ratio of 0.25:0.75 (mean=10.8000) has the fastest time of clotting and that with a concentration ratio of 0.50:0.50 (16.2000) is the slowest.  ANCOVA shows that there are significant difference in the time of clotting between the combined extracts of ginger and wirawer and the SST. The BUN and serum creatinine levels are relatively close to each other when the SST is compared with the combined extracts of ginger and wirawer. ANCOVA shows that there are no significant differences in the BUN and serum creatinine levels between the combined extracts of ginger and wirawer and the SST.  Hence, it can be concluded that there is a synergistic effect when ginger and wirawer extracts are combined for use as a procoagulant.

Keywords: Procoagulant properties, ginger, wirawer, blood clot, blood urea nitrogen


Haozhe Xie, Jie Li, Qiaosheng Zhang, Yadong Wang. Comparison among dimensionality reduction techniques based on Random Projection for cancer classification. Computational biology and chemistry, 65: 165-172, 2016. (IF=1.014)
[BibTeX] [Download PDF]
@article{xie2016comparison,
  title={Comparison among dimensionality reduction techniques based on Random Projection for cancer classification},
  author={Xie, Haozhe and Li, Jie and Zhang, Qiaosheng and Wang, Yadong},
  journal={Computational biology and chemistry},
  year={2016},
  pages={165--172},
  publisher={Elsevier}
}