Förster resonance energy transfer (FRET)-based biosensors are being fabricated for specific detection of biomolecules or changes in the microenvironment. FRET is a non-radiative transfer of energy from an excited donor fluorophore molecule to a nearby acceptor fluorophore molecule. In a FRET-based biosensor, the donor and acceptor molecules are typically fluorescent proteins or fluorescent nanomaterials such as quantum dots (QDs) or small molecules that are engineered to be in close proximity to each other. When the biomolecule of interest is present, it can cause a change in the distance between the donor and acceptor, leading to a change in the efficiency of FRET and a corresponding change in the fluorescence intensity of the acceptor. This change in fluorescence can be used to detect and quantify the biomolecule of interest. FRET-based biosensors have a wide range of applications, including in the fields of biochemistry, cell biology, and drug discovery. This review article provides a substantial approach on the FRET-based biosensor, principle, applications such as point-of-need diagnosis, wearable, single molecular FRET (smFRET), hard water, ions, pH, tissue-based sensors, immunosensors, and aptasensor. Recent advances such as artificial intelligence (AI) and Internet of Things (IoT) are used for this type of sensor and challenges.

In the rapidly evolving landscape of nanomedicine, aptamers have emerged as powerful molecular tools, demonstrating immense potential in targeted therapeutics, diagnostics, and drug delivery systems. This paper explores the computational features of aptamers in nanomedicine, highlighting their advantages over antibodies, including selectivity, low immunogenicity, and a simple production process. A comprehensive overview of the aptamer development process, specifically the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) process, sheds light on the intricate methodologies behind aptamer selection. The historical evolution of aptamers and their diverse applications in nanomedicine are discussed, emphasizing their pivotal role in targeted drug delivery, precision medicine and therapeutics. Furthermore, we explore the integration of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), Internet of Medical Things (IoMT), and nanotechnology in aptameric development, illustrating how these cutting-edge technologies are revolutionizing the selection and optimization of aptamers for tailored biomedical applications. This paper also discusses challenges in computational methods for advancing aptamers, including reliable prediction models, extensive data analysis, and multiomics data incorporation. It also addresses ethical concerns and restrictions related to AI and IoT use in aptamer research. The paper examines progress in computer simulations for nanomedicine. By elucidating the importance of aptamers, understanding their superiority over antibodies, and exploring the historical context and challenges, this review serves as a valuable resource for researchers and practitioners aiming to harness the full potential of aptamers in the rapidly evolving field of nanomedicine.

Metabolites are the intermediatory products of metabolic processes catalyzed by numerous enzymes found inside the cells. Detecting clinically relevant metabolites is important to understand their physiological and biological functions along with the evolving medical diagnostics. Rapid advances in detecting the tiny metabolites such as biomarkers that signify disease hallmarks have an immense need for high-performance identifying techniques. Low concentrations are found in biological fluids because the metabolites are difficult to dissolve in an aqueous medium. Therefore, the selective and sensitive study of metabolites as biomarkers in biological fluids is problematic. The different non-electrochemical and conventional methods need a long time of analysis, long sampling, high maintenance costs, and costly instrumentation. Hence, employing electrochemical techniques in clinical examination could efficiently meet the requirements of fully automated, inexpensive, specific, and quick means of biomarker detection. The electrochemical methods are broadly utilized in several emerging and established technologies, and electrochemical biosensors are employed to detect different metabolites. This review describes the advancement in electrochemical sensors developed for clinically associated human metabolites, including glucose, lactose, uric acid, urea, cholesterol, etc., and gut metabolites such as TMAO, TMA, and indole derivatives. Different sensing techniques are evaluated for their potential to achieve relevant degrees of multiplexing, specificity, and sensitivity limits. Moreover, we have also focused on the opportunities and remaining challenges for integrating the electrochemical sensor into the point-of-care (POC) devices.

In the current work, biosynthesis of silver nanoparticle (Ag NPs) and interaction study between alpha amylase and Ag NPs/nanocluster has been performed via wet-lab as well as in silico approach. We have synthesized Ag NPs using alpha amylase enzyme which reduces the silver nitrate precursor forming the stable Ag NPs. UV-Visible spectroscopy and fluorescence spectroscopies were performed for optical characterization of Ag NPs. UV-Vis spectra showed the wide absorption band centered around 475 nm due to surface plasmon resonance. We have also observed gradual decrease in fluorescence intensity with the increase in incubation time. Also, shift in λmax of the emission spectra was recorded which clearly suggested the formation of nano-bio-conjugate. Circular dichroism spectra show the initial decrease in the ellipticity, when we added the silver nitrate, but after incubating for different time, there are no major changes in secondary structure of protein. In computational study we have modelled ground state configuration of (Ag) ₂₄ nanocluster using in silico approach. Further docking of the modelled optimized nanocluster with alpha amylase was performed and found that Ag-nanocluster showing non-covalent interaction with alpha amylase and forming stable docking complex.

Trimethyleneamine N-oxide (TMAO); a gut microbiota derived metabolite has been involved in human health and diseases. It is enhanced by insulin resistivity and linked with various metabolic syndromes in human being such as renal, neuro-degenerative, and cardiovascular diseases. The primary mechanism through which TMAOs promotes disease is not clear yet. TMAO with MW= 75.11 g/mol is a small biomolecule hence, it becomes crucial to develop the conjugate of TMAO with BSA for aptamer synthesis. The binding interactions among TMAO and BSA were investigated using spectroscopic methods like UV-Vis, photoluminescence, Fourier transform infrared and circular dichroism. Hydrophilicity/Hydrophobicity of the conjugate was monitored by using contact angle (Ɵ) measurement. Sodium dodecyl sulphate polyacryl amide gel electrophoresis (SDS-PAGE) confirmed the different ratio of conjugate formation with the help of band size. This interaction study reveals that TMAO bind with BSA on two sites and with high affinity on one site. Docking studies also showed TMAO is involved in non-covalent interaction with bovine serum albumin forming stable docking complex with binding score of –3.6 kcal/mol obtained from the docking simulation. TMAO is involved in interaction with BSA via amino acid residues forming the stable docking complex through hydrogen bond and electrostatic interaction. This kind of interaction study may be helpful in making strategies to break the conjugation between serum albumin and uremic toxin and pave the way for the treatment for CKD and other diseases wherein TMAO is implicated. Also, conjugation of TMAO and BSA studied here may also serve as premise to develop aptamers for the detection of TMAO in the body fluids.