The ability to monitor diseases, therapies, and their effects on the body is a critical component of modern care and personalized medicine. Real time monitoring can be achieved by analyzing body fluids or by applying sensors on, or alternatively, inside the body. Implantable sensors, however, must be removed. Second removal procedures lead to further tissue damage, which can be a problem in tissues such as those of the central nervous system. The use of biodegradable sensors alleviates these problems since they do not require removal procedures. Recent advances in material science made it possible for all sensor components to be biodegradable. Small size and power of implants, and the limited selection of materials are the main constraints determining the capabilities of the biodegradable device. Thus, the design will be always a challenge exploring a trade-off among these parameters. Despite of the encouraging results illustrating that biodegradable sensors can be as accurate and reliable as commercially available nondegradable ones, biodegradable implantable sensors are still in their infancy. Significant advances made in this area are critically reviewed in this paper, and future prospects are highlighted.

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.

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