Scutellaria barbata is a perennial herb which was vastly prescribed in Chinese medicine to treat inflammations, infections and it is also used a detoxifying agent. We synthesized silver nanoparticles with Scutellaria barbata extract and characterized the nanoparticles with UV-Vis spectroscopic analysis, TEM, AFM, FTIR and XRD. The biofilm inhibiting property of synthesized silver nanoparticles were examined with XTT reduction assay and the antimicrobial property was examined with well diffusion method. The silver nanoparticles were also coated with cotton fabrics and their efficacy against antimicrobials was analyzed to prove its application. The cytotoxic property of synthesized silver nanoparticles was examined with L929 fibroblast cells using MTT assay. Finally we analyzed the wound healing property of synthesized silver nanoparticles with wound scratch assay. The result of our UV-Vis spectroscopic analysis confirms Scutellaria barbata aqueous extract reduced silver ions and synthesized silver nanoparticles. The characterization studies TEM, AFM, FTIR and XRD confirms the synthesized silver nanoparticles are in ideal shape and size to be utilized as a drug. The XTT reduction assay proves silver nanoparticles effectively inhibits the biofilm formation in both resistant and sensitive strains. Antimicrobial sensitivity tests confirms synthesized silver nanoparticles and cotton coated synthesized silver nanoparticles both are effective against gram positive, gram negative and fungal species. Further the results of MTT assay confirms the synthesized silver nanoparticles are non toxic and finally the wound healing potency of the nanoparticles was confirmed with wound scratch assay. Over all our results authentically confirms the silver nanoparticles synthesized with Scutellaria barbata aqueous extract is potent wound healing drug.
Although starch has been intensively studied as a raw material for 3D printing, the relationship between several important process parameters in the preparation of starch gels and the printing results is unclear. In this study, the relationship between different processing conditions and the gel printing performance of corn starch was evaluated by printing tests, rheological tests and low-field nuclear magnetic resonance (LF-NMR) tests, and a back-propagation artificial neural network (BP-ANN) model for predicting gel printing performance was developed. The results revealed that starch gels exhibited favorable printing performance when the gelatinization temperature ranged from 75 °C to 85 °C, and the starch content was maintained between 15 % and 20 %. The R2adj of the BP-ANN models were all reached 0.894, which indicated good predictive ability. The results of the study not only provide theoretical support for the application of corn starch gels in 3D food printing, but also present a novel approach for predicting the printing performance of related materials. This method contributes to the optimization of printing parameters, thereby enhancing printing efficiency and quality.