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Biological Activities and Host-Response Insights of Ocimum basilicum Flower-Derived Silver Nanoparticles

Aug 2026 · Inorganics · 0 citations · 44 references

TL;DR

In this study, Ocimum basilicum flower extracts used for the synthesis of silver nanoparticles (OBF-AgNPs) and evaluated for antimicrobial and antibiofilm activities demonstrated antimicrobial and antibiofilm activities.

Abstract

Antimicrobial resistance and the limitations of conventional antimicrobial therapies have intensified the search for multifunctional nanoscale agents. Plant flowers are effective and sustainable biological materials for the eco-friendly synthesis of stable nanoparticles. In this study, Ocimum basilicum flower (OBF) extracts were used for the synthesis of silver nanoparticles (OBF-AgNPs) and evaluated for antimicrobial and antibiofilm activities. Characterization by UV–Vis spectroscopy, FTIR, SEM, TEM, EDX, DLS, and zeta-potential analysis revealed an absorption maximum at 440 nm and predominantly spherical particles measuring 5–38 nm. The hydrodynamic diameter was 53.84 nm, with a PDI of 0.4123 and a zeta potential of −23.47 mV. OBF-AgNPs showed greater antimicrobial activity than the crude extract and AgNO3, with MIC and MBC values of 97–194 and 388–776 µg/mL, respectively. Biofilm formation was inhibited against S. aureus, P. aeruginosa, and C. tropicalis in a concentration-dependent manner. Eight GC–MS-identified phytochemicals were separately examined through network pharmacology. Of 124 predicted targets, 34 overlapped with microbial-infection-related genes, and enrichment analysis highlighted inflammatory and immune-response pathways. Molecular docking against the network-derived host-target PTGS2 showed moderate predicted interactions compared with reference inhibitors, with τ-cadinol showing the lowest docking score of −7.7 kcal/mol among the tested phytochemicals. Overall, the synthesized OBF-AgNPs demonstrated antimicrobial and antibiofilm activities. The in silico analyses independently identified host-response-related computational insights from GC–MS-identified flower constituents.

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