Silver nanoparticles (AgNPs) exhibit strong antimicrobial properties against many pathogens. Traditionally employed chemical methods for AgNPs synthesis are toxic for the environment. Here, we report a quicker, simpler, and environmentally benign process to synthesize AgNPs by using an aqueous ‘root extract’ of Salvadora persica (Sp) plant as a reducing agent. The synthesized Salvadora persica nano particles (SpNPs) showed significantly higher antimicrobial efficacy compared to earlier reported studies. We characterized SpNPs using UV–Vis spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Transmission Electron Microscopy (TEM), Field Emission Scanning Electron Microscopy (FE-SEM), Dynamic Light Scattering (DLS) and X-ray powder diffraction (P-XRD). UV–Vis spectrum showed the highest absorbance at 420 nm. FTIR analysis depicts presence of bond stretching including OH– (3300 cm⁻¹), C=N– (2100 cm⁻¹) and NH– (1630 cm⁻¹) which are attributed in the involvement of phenolics, proteins or nitrogenous compounds in reduction and stabilization of AgNPs. TEM, FE-SEM and DLS analysis revealed the spherical and rod nature of SpNPs and an average size of particles as 37.5 nm. XRD analysis showed the presence of the cubic structure of Ag which confirmed the synthesis of silver nanoparticles. To demonstrate antimicrobial efficacy, we evaluated SpNPs antimicrobial activity against two bacterial pathogens (Escherichia coli (ATCC 11229) and Staphylococcus epidermidis (ATCC 12228)). SpNPs showed a significantly high inhibition for both pathogens and minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were found to be 0.39 µg/mL and 0.78 µg/mL for E. coli while 0.19 µg/mL and 0.39 µg/mL for S. epidermidis respectively. Further, Syto 16 staining of bacterial cells provided a supplemental confirmation of the antimicrobial efficacy as the bacterial cells treated with SpNPs stop to fluoresce compared to the untreated bacterial cells. Our highly potent SpNPs will likely have a great potential for many antimicrobial applications including wound healing, water purification, air filtering and other biomedical applications.

Portable smartphone-based fluorescent microscopes are becoming popular owing to their ability to provide major functionalities offered by regular benchtop microscopes at a fraction of the cost. However, smartphone-based microscopes are still limited to a single fluorophore, fixed magnification, the inability to work with a different smartphones, and limited usability to either glass slides or cover slips. To overcome these challenges, here we present a modular smartphone-based microscopic attachment. The modular design allows the user to easily swap between different sets of filters and lenses, thereby enabling utility of multiple fluorophores and magnification levels. Our microscopic smartphone attachment can also be used with different smartphones and was tested with Nokia Lumia 1020, Samsung Galaxy S9+, and an iPhone XS. Further, we showed imaging results of samples on glass slides, cover slips, and microfluidic devices. A 1951 USAF resolution test target was used to quantify the maximum resolution of the microscope which was found to be 3.9 μm. The performance of the smartphone-based microscope was compared with a benchtop microscope and we found an R² value of 0.99 using polystyrene beads and blood cells isolated from human blood samples collected from Robert Wood Johnson Medical Hospital. Additionally, to count the particles (cells and beads) imaged from the smartphone-based fluorescent microscope, we developed artificial neural networks (ANNs) using multiple training algorithms, and evaluated their performances compared to the control (ImageJ). Finally, we did ANOVA and Tukey's post-hoc analysis and found a p-value of 0.97 which shows that no statistical significant difference exists between the performance of the trained ANN and control (ImageJ).

Silver nanoparticles (AgNPs) gained significant attention due to their activity against microbial pathogens, cancer cells, and viral particles etc. Traditional fabrication methods require hazardous chemicals as reducing agents and their usage and disposal pose a significant hazard to environmental ecosystem. Here, a de novo, robust, cost effective and an eco-friendly method is reported to fabricate AgNPs irradiated with sunlight (SL) while using Salvadora persica root extract (SPE) as reducing agent. Sunlight (SL) irradiated S. persica silver nanoparticles (SpNPs) i.e., SL-SpNPs were characterized using multiple techniques and their antibacterial efficacy was evaluated. The SL-SpNPs were synthesized in 10 min. Field emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM) analysis revealed their spherical morphology with a size range of 4.5–39.7 nm, while surface plasmon resonance (SPR) peaked at 425 nm. Fourier transform infrared spectroscopy (FTIR) analysis suggested that the reduction of SL-SpNPs was due to the presence of phytochemicals in the SPE. Furthermore, X-ray powder diffraction (P-XRD) pattern depicted the crystal structure of SL-SpNPs, hence proving the presence of AgNPs. Further the antibacterial studies were carried out against Escherichia coli (ATCC 11229) and Staphylococcus epidermidis (ATCC 12228) using Kirby Bauer method. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) for E. coli were determined to be 1.5 μg/mL and 3.0 μg/mL respectively while MIC and MBC values for S. epidermidis were found to be 12.5 μg/mL and 25 μg/mL respectively. The solar irradiation-based fabrication method and resulting SL-SpNPs can find their utility in many biomedical and environmental applications.

Leukocyte quantification from whole blood can aid in detecting and managing infections, cardiovascular diseases, and immune system responses. In addition to traditional leukocyte quantification devices such as flow cytometers and benchtop fluorescent microscopes, smartphone-based particle quantifiers are becoming popular because they provide results at a fraction of the cost. One major limitation of these smartphone-based devices is their dependence on desktop computers for data processing, which keeps them from reaching their true translation potential as point of care (POC) devices. In this paper, we present a computer vision and machine learning-enabled methodology to count particles imaged from our 3D printed smartphone-coupled fluorescent microscope. Multiple convolution neural networks (CNN) using different filter sizes were implemented and trained with various learning rates (0.001, 0.0001) and batch sizes (8,16,32). The performance of these trained networks was then tested on green fluorescent microparticles and leukocytes and compared against the ground truth obtained using ImageJ. An R ² value of 0.99 was observed. Next, when cross-validation was done to validate the efficacy of the designed CNN architecture, and the predicted results showed a good correlation (R ² = 0.99) when compared against the ground truth. The performance of the trained model was also evaluated on particles conjugated with a different fluorophore and an R ² value of 0.99 was observed, showcasing its efficacy and versatility. This trained model was then integrated into an Application Programming Interface (API) and is available online for the broader community usage.