At present, aerogel is one of the most interesting materials globally. The network of aerogel consists of pores with nanometer widths, which leads to a variety of functional properties and broad applications. Aerogel is categorized as inorganic, organic, carbon, and biopolymers, and can be modified by the addition of advanced materials and nanofillers. Herein, this review critically discusses the basic preparation of aerogel from the sol-gel reaction with derivation and modification of a standard method to produce various aerogels for diverse functionalities. In addition, the biocompatibility of various types of aerogels were elaborated. Then, biomedical applications of aerogel were focused on this review as a drug delivery carrier, wound healing agent, antioxidant, anti-toxicity, bone regenerative, cartilage tissue activities and in dental fields. The clinical status of aerogel in the biomedical sector is shown to be similarly far from adequate. Moreover, due to their remarkable properties, aerogels are found to be preferably used as tissue scaffolds and drug delivery systems. The advanced studies in areas including self-healing, additive manufacturing (AM) technology, toxicity, and fluorescent-based aerogel are crucially important and are further addressed.
Tuberculosis (TB) remains one of the most devastating infectious diseases and its treatment efficiency is majorly influenced by the stage at which infection with the TB bacterium is diagnosed. The available methods for TB diagnosis are either time consuming, costly or not efficient. This study employs a signal generation mechanism for biosensing, known as Plasmonic ELISA, and computational intelligence to facilitate automatic diagnosis of TB. Plasmonic ELISA enables the detection of a few molecules of analyte by the incorporation of smart nanomaterials for better sensitivity of the developed detection system. The computational system uses k-means clustering and thresholding for image segmentation. This paper presents the results of the classification performance of the Plasmonic ELISA imaging data by using various types of classifiers. The five-fold cross-validation results show high accuracy rate (>97%) in classifying TB images using the entire data set. Future work will focus on developing an intelligent mobile-enabled expert system to diagnose TB in real-time. The intelligent system will be clinically validated and tested in collaboration with healthcare providers in Malaysia.
Over recent years, carbon quantum dots (CQDs) have advanced significantly and gained substantial attention for their numerous benefits. These benefits include their simple preparation, cost-effectiveness, small size, biocompatibility, bright luminescence, and low cytotoxicity. As a result, they hold great potential for various fields, including bioimaging. A fascinating aspect of synthesizing CQDs is that it can be accomplished by using biomass waste as the precursor. Furthermore, the synthesis approach allows for control over the physicochemical characteristics. This paper unequivocally examines the production of CQDs from biomass waste and their indispensable application in bioimaging. The synthesis process involves a simple one-pot hydrothermal method that utilizes biomass waste as a carbon source, eliminating the need for expensive and toxic reagents. The resulting CQDs exhibit tunable fluorescence and excellent biocompatibility, making them suitable for bioimaging applications. The successful application of biomass-derived CQDs has been demonstrated through biological evaluation studies in various cell lines, including HeLa, Cardiomyocyte, and iPS, as well as in medaka fish eggs and larvae. Using biomass waste as a precursor for CQDs synthesis provides an environmentally friendly and sustainable alternative to traditional methods. The resulting CQDs have potential applications in various fields, including bioimaging.