Aptasensors have attracted considerable interest and widespread application in point-of-care testing worldwide. One of the biggest challenges of a point-of-care (POC) is the reduction of treatment time compared to central facilities that diagnose and monitor the applications. Over the past decades, biosensors have been introduced that offer more reliable, cost-effective, and accurate detection methods. Aptamer-based biosensors have unprecedented advantages over biosensors that use natural receptors such as antibodies and enzymes. In the current epidemic, point-of-care testing (POCT) is advantageous because it is easy to use, more accessible, faster to detect, and has high accuracy and sensitivity, reducing the burden of testing on healthcare systems. POCT is beneficial for daily epidemic control as well as early detection and treatment. This review provides detailed information on the various design strategies and virus detection methods using aptamer-based sensors. In addition, we discussed the importance of different aptamers and their detection principles. Aptasensors with higher sensitivity, specificity, and flexibility are critically discussed to establish simple, cost-effective, and rapid detection methods. POC-based aptasensors' diagnostic applications are classified and summarised based on infectious and infectious diseases. Finally, the design factors to be considered are outlined to meet the future of rapid POC-based sensors.
Metal-Organic Frameworks (MOFs) have exceptional inherent properties that make them highly suitable for diverse applications, such as catalysis, storage, optics, chemo sensing, and biomedical science and technology. Over the past decades, researchers have utilized various techniques, including solvothermal, hydrothermal, mechanochemical, electrochemical, and ultrasonic, to synthesize MOFs with tailored properties. Post-synthetic modification of linkers, nodal components, and crystallite domain size and morphology can functionalize MOFs to improve their aptamer applications. Advancements in AI and machine learning led to the development of nonporous MOFs and nanoscale MOFs for medical purposes. MOFs have exhibited promise in cancer therapy, with the successful accumulation of a photosensitizer in cancer cells representing a significant breakthrough. This perspective is focused on MOFs' use as advanced materials and systems for cancer therapy, exploring the challenging aspects and promising features of MOF-based cancer diagnosis and treatment. The paper concludes by emphasizing the potential of MOFs as a transformative technology for cancer treatment and diagnosis.
With increasing population there is a rise in pathological diseases that the healthcare facilities are grappling with. Sweat-based wearable technologies for continuous monitoring have overcome the demerits associated with sweat sampling and sensing. Hence, sweat as an alternative biofluid holds great promise for the quantification of a host of biomarkers and understanding the functioning of the body, thereby deducing ailments quickly and economically. This comprehensive review accounts for recent advances in sweat-based LOCs (Lab-On-Chips), which are a likely alternative to the existing blood-urea sample testing that is invasive and time-consuming. The present review is focused on the advancements in sweat-based Lab-On-Chips (LOCs) as an alternative to invasive and time-consuming blood-urea sample testing. In addition, different sweat collection methods (direct skin, near skin and microfluidic) and their mechanism for urea sensing are explained in detail. The mechanism of urea in biofluids in protein metabolism, balancing nitrogen levels and a crucial factor of kidney function is described. In the end, research and technological advancements are explained to address current challenges and enable its widespread implementation.
Single-cell analysis (SCA) improves the detection of cancer, the immune system, and chronic diseases from complicated biological processes. SCA techniques generate high-dimensional, innovative, and complex data, making traditional analysis difficult and impractical. In the different cell types, conventional cell sequencing methods have signal transformation and disease detection limitations. To overcome these challenges, various deep learning techniques (DL) have outperformed standard state-of-the-art computer algorithms in SCA techniques. This review discusses DL application in SCA and presents a detailed study on improving SCA data processing and analysis. Firstly, we introduced fundamental concepts and critical points of cell analysis techniques, which illustrate the application of SCA. Secondly, various effective DL strategies apply to SCA to analyze data and provide significant results from complex data sources. Finally, we explored DL as a future direction in SCA and highlighted new challenges and opportunities for the rapidly evolving field of single-cell omics.