Since the early 1990s, nanotechnology has led to new horizons in nanomedicine, which encompasses all spheres of science including chemistry, material science, biology, and biotechnology. Emerging viral infections are creating severe hazards to public health worldwide, recently, COVID-19 has caused mass human casualties with significant economic impacts. Interestingly, silver nanoparticles (AgNPs) exhibited the potential to destroy viruses, bacteria, and fungi using various methods. However, developing safe and effective antiviral drugs is challenging, as viruses use host cells for replication. Designing drugs that do not harm host cells while targeting viruses is complicated. In recent years, the impact of AgNPs on viruses has been evaluated. Here, we discuss the potential role of silver nanoparticles as antiviral agents. In this review, we focus on the properties of AgNPs such as their characterization methods, antiviral activity, mechanisms, applications, and toxicity.

Atrial fibrillation (AF) is one of the most widespread cardiovascular diseases and impacts the overall general population of the world. Most of the current techniques are based on hand-crafted features for automatic AF classification. The primary task of this work is to design a deep learning-based approach that will eliminate the necessity of manual identification of features. We have used a pre-trained convolutional neural network (CNN), namely AlexNet, to train using 5,655 single-lead ECG recordings. Initially, we have extracted a spectrogram for all 30s signals and converted them to RGB images with Continuous Wavelet Transform (CWT); later fed to transferred AlexNet and trained with some changes in specifications. The findings of the study indicate that our technique attains a state-of-the-art accuracy of 97.9% and an F1 score of 98.82% while having higher overall sensitivity (98.9%) and specificity (90.7%) and outperformed all existing methods.

Idiopathic pulmonary fibrosis (IPF) is a restrictive interstitial lung disease that causes lung function decline by lung tissue scarring. Although lung function decline is assessed by the forced vital capacity (FVC), determining the accurate progression of IPF remains a challenge. To address this challenge, we proposed Fibro-CoSANet, a novel end-to-end multi-modal learning based approach, to predict the FVC decline. Fibro-CoSANet utilized computed tomography images and demographic information in convolutional neural network frameworks with a stacked attention layer. Extensive experiments on the OSIC Pulmonary Fibrosis Progression Dataset demonstrated the superiority of our proposed Fibro-CoSANet by achieving new state-of-the-art modified Laplace log-likelihood score of −6.68. This network may benefit research areas concerned with designing networks to improve the prognostic accuracy of IPF. The source-code for Fibro-CoSANet is available at: https://github.com/zabir-nabil/Fibro-CoSANet.