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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.

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

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