Synthesis, characterization and dose dependent antimicrobial and anti-cancerous activity of phycogenic silver nanoparticles against human hepatic carcinoma (HepG2) cell line

In the present study silver nanoparticles (AgNPs) were successfully synthesized using aqueous extract of sea weed, Gracilaria corticata. The aqueous callus extract (5%) treated with 1 mM silver nitrate solution resulted in the formation of AgNPs and the surface plasmon resonance (SPR) of the formed AgNPs was recorded at 405 nm using UV-Visible spectrophotometer. The molecules involved in the formation of AgNPs were identified by Fourier transform infrared spectroscopy (FT-IR), surface morphology was studied by using scanning electron microscopy (SEM), and X-ray diffraction spectroscopy (XRD) was used to determine the crystalline structure. SEM micrograph clearly revealed the size of the AgNPs was in the range of 20–55 nm with spherical, hexagonal in shape and poly-dispersed nature. High positive Zeta potential (22.9 mV) of formed AgNPs indicates the stability and XRD pattern revealed the crystal structure of the AgNPs by showing the Bragg’s peaks corresponding to (111), (200), (220) planes of face-centered cubic crystal phase of silver. The synthesized AgNPs exhibited effective anticancerous activity (at doses 6.25 and 12.5 µg/ml of AgNPs) against human hepatic carcinoma cell line (HepG2)

Hybrid deep recurrent neural networks for COVID-19 detection and diagnosis

Corona virus Disease (COVID-19) is an acute pandemic which has put the lives of millions of people worldwide at risk during recent times. There is a high demand to develop effective tools and methods to diagnose the COVID infection in people at an early stage to prevent the spread of the disease to a larger community. This paper aims to provide a systematic method for COVID diagnosis using machine learning and deep learning algorithms. The proposed method Hybrid Deep Recurrent Neural Network (HDRNN) is a fusion of Convolution Neural Networks (CNN) and Long Short-Term Memory-Recurrent Neural Networks (LSTM-RNN) to detect COVID infection efficiently from X-ray samples. CNN is employed in the proposed method primarily to extract the essential features from the X-ray images and LSTM is suitable to classify the COVID affected patients with more fidelity. The dataset used in this work consists of an aggregate of 3470 images including COVID affected and Pneumonia affected samples. The experimental results carried out on the collected dataset with the proposed HDRNN method demonstrated an accuracy of 99.4%, F1 Score 98.7%, Sensitivity of 99.3% and Specificity of 99.2 %.&nbsp