Unnatural amino acids have gained significant attention in protein engineering and drug discovery as they allow the evolution of proteins with enhanced stability and activity. The incorporation of unnatural amino acids into proteins offers a rational approach to engineer enzymes for designing efficient biocatalysts that exhibit versatile physicochemical properties and biological functions. This review highlights the biological and synthetic routes of unnatural amino acids to yield a modified protein with altered functionality and their incorporation methods. Unnatural amino acids offer a wide array of applications such as antibody-drug conjugates, probes for change in protein conformation and structure–activity relationships, peptide-based imaging, antimicrobial activities, etc. Besides their emerging applications in fundamental and applied science, systemic research is necessary to explore unnatural amino acids with novel side chains that can address the limitations of natural amino acids.

In the middle of an ever‐changing landscape of diabetes care, precision medicine, and lifestyle therapies are becoming increasingly important. Dietary polyphenols are like hidden allies found in our everyday meals. These biomolecules, found commonly in fruits, vegetables, and various plant‐based sources, hold revolutionary potential within their molecular structure in the way we approach diabetes and its intimidating consequences. There are currently numerous types of diabetes medications, but they are not appropriate for all patients due to limitations in dosages, side effects, drug resistance, a lack of efficacy, and ethnicity. Currently, there has been increased interest in practicing herbal remedies to manage diabetes and its related complications. This article aims to summarize the potential of dietary polyphenols as a foundation in the treatment of diabetes and its associated consequences. We found that most polyphenols inhibit enzymes linked to diabetes. This review outlines the potential benefits of selected molecules, including kaempferol, catechins, rosmarinic acid, apigenin, chlorogenic acid, and caffeic acid, in managing diabetes mellitus as these compounds have exhibited promising results in in vitro, in vivo, in silico, and some preclinical trials study. This encompassing exploration reveals the multifaceted impact of polyphenols not only in mitigating diabetes but also in addressing associated conditions like inflammation, obesity, and even cancer. Their mechanisms involve antioxidant functions, immune modulation, and proinflammatory enzyme regulation. Furthermore, these molecules exhibit anti‐tumor activities, influence cellular pathways, and activate AMPK pathways, offering a less toxic, cost‐effective, and sustainable approach to addressing diabetes and its complications.

Development of ionic liquid (IL)-based colorimetric sensor arrays for detection and identification of chemicals in both the aqueous and vapor phases is reported. These facile and inexpensive optoelectronic sensors were fabricated by using ionic liquids (ILs) derived from readily available pH indicator dyes. A series of 12 different chemosensory ILs were synthesized by pairing anionic pH indicator dyes with trihexyl(tetradecyl)phosphonium ([P₆₆₆₁₄]) cation via an ion exchange reaction. The incorporation of the [P₆₆₆₁₄] cation imparted hydrophobic characteristics to these ILs, and this induced hydrophobicity led to their desired low solubility in aqueous solutions, as well as eliminated the need for a specialized hydrophobic matrix/substrate for immobilization. In this manuscript, four different matrices, i.e. glass microfiber filter papers, cotton threads, silica thin layer chromatography (TLC) plates, and alumina TLC plates, were employed for fabrication of sensor arrays. These sensor arrays were used to analyze pH values of aqueous solutions as well as for detection of acidic and basic vapors. To further prove the applicability of these IL sensor arrays as tools to sense closely related complex materials, the arrays were applied to successful discrimination of aqueous solutions of smoke from three commercially available cigarettes. The digital data generated from these sensor arrays were used in developing predictive models for accurately identifying various analytes. Two approaches were used for developing the models, and two methods were applied for assessing the predictive accuracy of the models. Use of cotton threads as a matrix led to development of a more flexible, low volume, and lightweight array to estimate pH and detect a variety of vapors. These wearable arrays may possibly be incorporated into bandages, sweatbands, diapers, and similar systems. Overall, these IL-based sensor arrays should provide a new research direction in the development of advanced colorimetric sensor arrays for detection and identification of a range of analytes relevant to many different applications.