Back

Assessment of antimicrobial activity of insects products and nests used in traditional medicine in Burkina Faso.

Ouango, M.; CISSE, H.; ROMBA, R.; DRABO, S. F.; SEMDE, R.; Aly, S.; GNANKINE, O.

2023-11-25 microbiology
10.1101/2023.11.25.568644 bioRxiv
Show abstract

In Burkina Faso, products and insect nests are used for therapeutic purposes in traditional medicine. However, this use by local populations is marginal and empirical. Our study aimed at evaluating the antimicrobial activity of insect products and their nests. For this purpose, the collected insect products and nests were finely ground. Hydroethanolic extraction of bioactive molecules with potential antibacterial activity was performed according to standard methods. The solid medium diffusion method was used to test the antibacterial activity of hydroethanolic extracts of honey bee, bee wax, propolis, and termite nests against 22 pathogenic strains by inhibition diameter. Imipenem was used as a positive control. The extraction yields varied from 7.33% to 35.38% depending on the content of soluble matter. All products extracts and insect nests tested showed inhibitory activities. The inhibition diameters varied depending on the extract and strain tested. The largest diameter of inhibition (26{+/-}0.0 mm) was obtained using the nest extract of Macrotermes bellicosus against Salmonella Typhimurium ATCC14028. The lowest diameter of inhibition was 07{+/-}0.0 mm obtained with honey extract against Pseudomonas aeruginosa ATCC27853. Index multi-resistance of the extracts tested were between 0.2 and 0.6. Interestingly, the inhibition diameters of certain products and nest extracts of insects were sometimes greater than those of imipenem against the strains tested. This study revealed the antimicrobial potential of termite nest extracts and hive products against pathogens.

Matching journals

The top 3 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.