It has been demonstrated that metalloenzymes can function as hybrid catalysts that possess both homogeneous and enzymatic properties. Enantioselectivity or chemo selectivity, for example, will add value to the hybrid catalyst. In the preparation of hybrid metalloenzymes, Schiff base complexes, which either function as homogeneous artificial enzymes or contribute to the host's structure, are the focus of this review. The specifics of hybrid catalysts appear crucial for catalysis advancement because this strategy can practically be utilized with any bio- or synthetic host or guest coordination complex. Future-oriented material fabrication technologies would aim to reproduce features characteristic to the natural materials into the synthetic ones. Since surface patterning techniques can mimic the desired surface design, a variety of bio-mimicking strategies are already utilized in the medical industry. By highlighting their advantages and potential utility for biomedical applications, we highlight the most common patterning techniques used to fabricate polymeric substrates with micro- or nano-features. First, we'll talk about top-down and bottom-up fabrication methods like photolithography, electron, proton, ion beam, block copolymer, soft lithography, and advanced techniques like scanning probe and particle lithography. Next, we'll talk about alternative patterning methods like DNA self-assembly or biomolecule crystallization. The studies that have already been published, as well as the potential applications of bio- and synthetic polymer-patterned substrates, as well as the analysis of molecule and cell-interface interactions, cell development, migration, and differentiation, are described in greater detail, with an emphasis on their application to blood disorders and circulating blood cells. The benefits of using such substrates as component parts in biosensing devices are summarized in the final chapter, with anticipated applications in medical diagnosis and clinical healthcare. For the above-mentioned reason, bitewing radiography is utilized for the purpose of providing initial caries detection. The early identification of dental caries is necessary for the appropriate treatments. The use of well-known neural network schemes and deep structured architectures in clinical imaging aids in processing the large number of images, has been actively researched in recent years, and provides competitive performance. As a result, techniques based on deep learning have developed remarkable diagnostic efficiency in the field of radiology. This paper aims to use deep learning effectively for segmenting dental caries as a result of this emerging intelligence. Initially, during the pre-processing phase, contrast enhancement via Contrast Limited Adaptive Histogram Equalization and noise filtering via bilateral filtering are carried out. In addition, Fused Optimal Centroid K-means with K-Mediods Clustering is used to segment the caries, which is enhanced by Hybrid Sea Lion-Squirrel Search Optimization to produce the best parameter optimization. Morphological operations are used to perform the post-pre-processing of the images after the caries have been segmented. Last but not least, the meta-heuristic-based ResneXt with Recurrent Neural Network uses the segmented image to detect caries. The HSLnSSO algorithm modifies the architecture. For caries detection the new segmentation model and the well-trained MResneXtRNN have performed better than the traditional methods.

With Regards,
Sara Giselle
Associate Managing Editor
Global  Journal of Digestive Diseases