Natural enzymes possess several drawbacks which limits their application in industries, wastewater remediation and biomedical field. Therefore, in recent years researchers have developed enzyme mimicking nanomaterials and enzymatic hybrid nanoflower which are alternatives of enzyme. Nanozymes and organic inorganic hybrid nanoflower have been developed which mimics natural enzymes functionalities such as diverse enzyme mimicking activities, enhanced catalytic activities, low cost, ease of preparation, stability and biocompatibility. Nanozymes include metal and metal oxide nanoparticles mimicking oxidases, peroxidases, superoxide dismutase and catalases while enzymatic and non-enzymatic biomolecules were used for preparing hybrid nanoflower. In this review nanozymes and hybrid nanoflower have been compared in terms of physiochemical properties, common synthetic routes, mechanism of action, modification, green synthesis and application in the field of disease diagnosis, imaging, environmental remediation and disease treatment. We also address the current challenges facing nanozyme and hybrid nanoflower research and the possible way to fulfil their potential in future.
Alternative splicing (AS) is a crucial process in genetic information processing that generates multiple mRNA molecules from a single gene, producing diverse proteins. Accurate prediction of AS events is essential for understanding various physiological aspects, including disease progression and prognosis. Machine learning (ML) techniques have been widely employed in bioinformatics to address this challenge. However, existing models have limitations in capturing AS events in the presence of mutations and achieving high prediction performance. To overcome these limitations, this research presents deep splicing code (DSC), a deep learning (DL)-based model for AS prediction. The proposed model aims to improve predictive ability by investigating state-of-the-art techniques in AS and developing a DL model specifically designed to predict AS events accurately. The performance of the DSC model is evaluated against existing techniques, revealing its potential to enhance the understanding and predictive power of DL algorithms in AS. It outperforms other models by achieving an average AUC score of 92%. The significance of this research lies in its contribution to identifying functional implications and potential therapeutic targets associated with AS, with applications in genomics, bioinformatics, and biomedical research. The findings of this study have the potential to advance the field and pave the way for more precise and reliable predictions of AS events, ultimately leading to a deeper understanding of genetic information processing and its impact on human physiology and disease.