Sustainable Solutions for Oral Health Monitoring: Biowaste-Derived Triboelectric Nanogenerator.

Oral healthcare monitoring is a vital aspect of identifying and addressing oral dental problems including tooth decay, gum pain, and oral cancer. Day by day, healthcare facilities and regular checkups are becoming more costly and time-consuming. In this context, consumers are moving toward advanced technology, such as bite sensors, to obtain regular data about their occlusal chewing patterns and strength. The triboelectric nanogenerator (TENG) can potentially eliminate the need for a battery by simply converting abundant vibrations from nature or human motion into electrical energy. In this work, biomaterials are obtained from biowastes such as cellulose from wood waste, chitosan from crab shells, and gelatin from fish scales. All wastes are biodegradable, and our work aims at sustainability and waste hierarchy. The single electrode mode-based TENG was designed and fabricated using biodegradable poly(vinyl alcohol) (PVA)-biomaterial composites, rice paper as a substrate, and edible silver leaf as an electrode. The highest electrical output was obtained for PVA/chitosan 10 wt % composite-based TENG (PC10) of about 20 V, 200 nA, and 12 nC. The biomechanical energy harvesting was measured, and powering of LED was demonstrated using a PC10 TENG device. A biocompatible bite sensor based on the TENG was used to measure the biting force of a dummy teeth model to demonstrate its potential use in dental health applications. It indicates the promising future value of disposable oral medication devices without any invasive surgery or injection.

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review.

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as 'dark chemical space' or 'dark chemistry.' Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: 'genomics', proteomics and 'in-silico simulation' will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.