The Potential Role of Ionic Liquid as a Multifunctional Dental Biomaterial.

In craniofacial research and routine dental clinical procedures, multifunctional materials with antimicrobial properties are in constant demand. Ionic liquids (ILs) are one such multifunctional intelligent material. Over the last three decades, ILs have been explored for different biomedical applications due to their unique physical and chemical properties, high task specificity, and sustainability. Their stable physical and chemical characteristics and extremely low vapor pressure make them suitable for various applications. Their unique properties, such as density, viscosity, and hydrophilicity/hydrophobicity, may provide higher performance as a potential dental material. ILs have functionalities for optimizing dental implants, infiltrate materials, oral hygiene maintenance products, and restorative materials. They also serve as sensors for dental chairside usage to detect oral cancer, periodontal lesions, breath-based sobriety, and dental hard tissue defects. With further optimization, ILs might also make vital contributions to craniofacial regeneration, oral hygiene maintenance, oral disease prevention, and antimicrobial materials. This review explores the different advantages and properties of ILs as possible dental material.

Web Search Engine Misinformation Notifier Extension (SEMiNExt): A Machine Learning Based Approach during COVID-19 Pandemic.

Misinformation such as on coronavirus disease 2019 (COVID-19) drugs, vaccination or presentation of its treatment from untrusted sources have shown dramatic consequences on public health. Authorities have deployed several surveillance tools to detect and slow down the rapid misinformation spread online. Large quantities of unverified information are available online and at present there is no real-time tool available to alert a user about false information during online health inquiries over a web search engine. To bridge this gap, we propose a web search engine misinformation notifier extension (SEMiNExt). Natural language processing (NLP) and machine learning algorithm have been successfully integrated into the extension. This enables SEMiNExt to read the user query from the search bar, classify the veracity of the query and notify the authenticity of the query to the user, all in real-time to prevent the spread of misinformation. Our results show that SEMiNExt under artificial neural network (ANN) works best with an accuracy of 93%, F1-score of 92%, precision of 92% and a recall of 93% when 80% of the data is trained. Moreover, ANN is able to predict with a very high accuracy even for a small training data size. This is very important for an early detection of new misinformation from a small data sample available online that can significantly reduce the spread of misinformation and maximize public health safety. The SEMiNExt approach has introduced the possibility to improve online health management system by showing misinformation notifications in real-time, enabling safer web-based searching on health-related issues.