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.
Several strategies have been proposed to improve the performance of the anaerobic digestion (AD) process. Among them, the use of various nanoparticles (NPs) (e.g. Fe, Ag, Cu, Mn, and metal oxides) is considered one of the most effective approaches to enhance the methanogenesis stage and biogas yield. Iron-based NPs (zero-valent iron with paramagnetic properties (Fe0) and iron oxides with ferromagnetic properties (Fe3O4/Fe2O3) enhance microbial activity and minimise the inhibition effect in methanogenesis. However, comprehensive and up-to-date knowledge on the function and impact of Fe-NPs on methanogens and methanogenesis stages in AD is frequently required. This review focuses on the applicative role of iron-based NPs (Fe-NPs) in the AD methanogenesis step to provide a comprehensive understanding application of Fe-NPs. In addition, insight into the interactions between methanogens and Fe-NPs (e.g. role of methanogens, microbe interaction and gene transfer with Fe-NPs) beneficial for CH4 production rate is provided. Microbial activity, inhibition effects and direct interspecies electron transfer through Fe-NPs have been extensively discussed. Finally, further studies towards detecting effective and optimised NPs based methods in the methanogenesis stage are reported.
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.
The augmentation of biogas production can be achieved by incorporating metallic nanoparticles as additives within anaerobic digestion. The objective of this current study is to examine the synthesis of Fe-Ni-Zn and Fe-Co-Zn trimetallic nanoparticles using the co-precipitation technique and assess its impact on anaerobic digestion using palm oil mill effluent (POME) as carbon source. The structural morphology and size of the synthesised trimetallic nanoparticles were analysed using a range of characterization techniques, such as X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), and Energy-dispersive X-ray spectroscopy (EDX) . The average size of Fe-Ni-Zn and Fe-Co-Zn were 19-25.5 nm and 19.1-30.5 nm respectively. Further, investigation focused on examining the diverse concentrations of trimetallic nanoparticles, ranging from 0 to 50 mgL-1. The biogas production increased by 55.55% and 60.11% with Fe-Ni-Zn and Fe-Co-Zn trimetallic nanoparticles at 40 mgL-1 and 20 mgL-1, respectively. Moreover, the lowest biogas of 11.11% and 38.11% were found with 10 mgL-1 of Fe-Ni-Zn and Fe-Co-Zn trimetallic nanoparticles. The findings of this study indicated that the trimetallic nanoparticles exhibited interactions with anaerobes, thereby enhancing the degradation process of palm oil mill effluent (POME) and biogas production. The study underscores the potential efficacy of trimetallic nanoparticles as a viable supplement for the promotion of sustainable biogas generation.
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.