Back

Experimental Urolithiasis Model to assess Phyto-fractions as Anti-lithiatic Contributors: A Herbaceutical Approach

Devi, A. T.; Yashaswini, N.; Zameer, F.; Prasad, N. M.

2021-06-01 biochemistry
10.1101/2021.06.01.446538 bioRxiv
Show abstract

Life-style disorders have bought a serious burden on the maintenance of health in animals and humans. Lithiasis specifically nephro- and urolithiasis is no exception and needs urgent attention. Currently, only semi-invasive and surgical methods are widely employed which leads to trauma and reoccurrence of kidney stones. Hence complementary and alternative herbal medicine could pave newer ways in exploring anti-lithiatic contributors. The current study attempts to screen twenty herbal hot aqueous leaf extracts for assessing their antioxidant potency (anti-stress) and efficiency against urolithiasis in an experimental calcium oxalate-induced in vitro (chicken egg membrane) model. The study was further validated by In silico molecular docking studies using the Molegro software package on enzymatic biomarkers involved in scavenging oxidants in the host and regulating oxalate metabolism at a cellular level. Among the screened botanicals Kalanchoe pinnata exhibited promising results compared to the standard chemical (potassium-magnesium citrate) and phyto-formulation drug (cystone) currently used by clinicians for treating urolithiasis. The phytochemical profiling (qualitative and quantitative) and virtual studies indicated rutin from Kalanchoe pinnata as a potential candidate for preventing kidney stones. The results of the current study provide better insights into the design and development of newer, smart, and cost-effective herbal therapeutics making food as medicine. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.