Deep learning-based classification of microalgae using light and scanning electron microscopy images.

Microalgae possess diverse applications, such as food production, animal feed, cosmetics, plastics manufacturing, and renewable energy sources. However, uncontrolled proliferation, known as algal bloom, can detrimentally impact ecosystems. Therefore, the accurate detection, monitoring, identification, and tracking of algae are imperative, albeit demanding considerable time, effort, and expertise, as well as financial resources. Deep learning, employing image pattern recognition, emerges as a practical and promising approach for rapid and precise microalgae cell counting and identification. In this study, we processed light microscopy (LM) and scanning electron microscopy (SEM) images of two Cyanobacteria species and three Chlorophyta species to classify them, utilizing state-of-the-art Convolutional Neural Network (CNN) models, including VGG16, MobileNet V2, Xception, NasnetMobile, and EfficientNetV2. In contrast to prior deep learning based identification studies limited to LM images, we, for the first time, incorporated SEM images of microalgae in our analysis. Both LM and SEM microalgae images achieved an exceptional classification accuracy of 99%, representing the highest accuracy attained by the VGG16 and EfficientNetV2 models to date. While NasnetMobile exhibited the lowest accuracy of 87% with SEM images, the remaining models achieved classification accuracies surpassing 93%. Notably, the VGG16 and EfficientNetV2 models achieved the highest accuracy of 99%. Intriguingly, our findings indicate that algal identification using optical microscopes, which are more cost-effective, outperformed electron microscopy techniques.

Apoptotic, cytotoxic, antioxidant, and antibacterial activities of biosynthesized silver nanoparticles from nettle leaf.

Here, we reported the biosynthesis of silver nanoparticles (AgNPs) using Urtica dioica (nettle) leaf extract as green reducing and capping agents and investigate their anticancer and antibacterial, activity. The Nettle-mediated biosynthesized AgNPs was characterized by UV-Vis a spectrophotometer. Their size, shape and elemental analysis were determined with the using of SEM and TEM. The crystal structure was determined by XRD and the biomolecules responsible for the reduction of Ag+ were determined using FTIR analysis. Nettle-mediated biosynthesis AgNPs indicated strong antibacterial activity against pathogenic microorganisms. Again, the antioxidant activity of AgNPs is quite high when compared to ascorbic acid. Anticancer effect of AgNPs, IC50 dose was determined by XTT analysis using MCF-7 cell line and the IC50 value was found to be 0.243 ± 0.014 µg/mL (% w/v).

Evaluation of antibacterial properties on polysulfone composite membranes using synthesized biogenic silver nanoparticles with Ulva compressa (L.) Kütz. and Cladophora glomerata (L.) Kütz. extracts.

Polysulfone (PS) composite membrane using green synthesized biogenic silver nanoparticles (Ag-NPs) with Ulva compressa (L.) Kütz. and Cladophora glomerata (L.) Kütz. extract were prepared by spin coating technique and are tested for antimicrobial activity using a direct contact test for the first time. Initially green synthesis of Ag-NPs was accomplished utilizing green macro algae i.e. U. compressa (L.) Kütz. and C. glomerata (L.) Kütz. by the reduction of AgNO3. The Ag-NPs/PS composite membranes from both algae revealed outstanding antimicrobial activity against all bacteria i.e. K. pneumonia, P. aeruginasa, E. coli, E. faecium and S. aureus. Bacterial growth was monitored for 17h with a temperature controlled microplate spectrophotometer. The kinetics of the outgrowth in each well were recorded continuously at 630nm every 60min. Thus present work remarkably offers a feasible, cheap and efficient alternative for making Ag-NPs and their utilization as antimicrobial agent on the PS composite membrane.