Back

Beneficial effects of two Ayurvedic formulations, Saraswata Ghrita and Kalyanaka Ghrita on survival and on toxic aggregates in Drosophila models of Huntington's and Alzheimer's disease

Sharma, S.; Singh, G.; Patwardhan, K.; Lakhotia, S. C.

2021-10-08 neuroscience
10.1101/2021.10.06.463232 bioRxiv
Show abstract

In order to understand the health promotive, rejuvenative and disease preventive approach of the Ayurvedic system of medicine in the light of current principles, we examined two Rasayana formulations, viz., Kalayanaka Ghrita (KG) and Saraswata Ghrita (SG) for their effects in Alzheimers (AD) and Huntingtons (HD) neurodegenerative disease models of Drosophila. Initial experiments involving feeding of wild type flies on food supplemented with 0.05%, 0.25% and 0.5% (w/v) KG or SG revealed 0.05% to be without any adverse effect while higher concentrations caused dose-dependent reduction in pupation frequency and adult life span in wild type flies. Rearing GMR-GAL4>127Q (HD model) and ey-GAL4>A{beta}42 (AD model) larvae and adults on 0.05% or 0.25% SG or KG supplemented food enhanced the otherwise significantly reduced larval lethality and enhanced their median life span, with the 0.25% SG or KG concentrations being less effective than the 0.05%. In parallel with the better larval survival and enhanced adult life span, feeding the HD and AD model larvae on either of the Ghrita supplemented food (0.05% and 0.25%) substantially reduced the polyQ aggregates or amyloid plaques, respectively, in the larval eye discs. The present first in vivo organismic model study results have clinical implications for the increasing burden of age-associated dementia and neurodegenerative diseases like AD and HD in human populations.

Matching journals

The top 9 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.