Browsing by Author "Ayipo, Y. O."

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    Genetic Diagnosis, Classification, and Risk Prediction in Cancer Using Next-Generation Sequencing in Oncology
    (CRC Press, Taylor & Francis Grou, 2024) Dauda, K. A.; Olorede, K. O.; Banjoko, A. W.; Yahya, W. B.; Ayipo, Y. O.
    In recent years, researchers have been overwhelmed with large-scale genomic data due to an advancement toward data-driven science called next-generation sequencing. Hence, it has been established that if the sequence of a gene is mutated, there is the possibility of unscheduled production of the protein, leading to cancer. The prediction of genes and variants into true and false pathogenic groups, extraction of the most influential genes, and identification of a pattern from this genomic data are of interest to biologists and medical professionals. However, the identification and extraction of these biomarkers and molecular signatures of different cancers among thousands of genes seem complex and problematic. Therefore, this chapter is aimed to develop an algorithm that can aid and provide efficient solutions for gene extraction, identification, and prediction in the realm of next-generation sequencing data. The proposed algorithm consists of three folds: the filtering fold using minimum redundancy maximum relevance (MRMR), the wrapper fold using the Boruta algorithm, and the last fold consisting of the use of deep learning (DL). A comparison assessment was performed on the proposed algorithm and the existing methods using five RNA-seq datasets from a cancer patient. The results revealed that the proposed algorithm significantly outperforms the existing methods in selecting fewer highly relevant genes for the cancer type while maintaining a high classification prediction accuracy.
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    Small-molecule inhibitors of bacterial-producing metallo-β-lactamases: insights into their resistance mechanisms and biochemical analyses of their activities
    (2023-03-31) Ayipo, Y. O.; Chong C. F.; Mordi, M. N.
    Antibiotic resistance (AR) remains one of the major threats to the global healthcare system, which is associated with alarming morbidity and mortality rates. The defence mechanisms of Enterobacteriaceae to antibiotics occur through several pathways including the production of metallo-β-lactamases (MBLs). The carbapenemases, notably, New Delhi MBL (NDM), imipenemase (IMP), and Verona integron-encoded MBL (VIM), represent the critical MBLs implicated in AR pathogenesis and are responsible for the worst ARrelated clinical conditions, but there are no approved inhibitors to date, which needs to be urgently addressed. Presently, the available antibiotics including the most active β-lactam-types are subjected to deactivation and degradation by the notorious superbug-produced enzymes. Progressively, scientists have devoted their efforts to curbing this global menace, and consequently a systematic overview on this topic can aid the timely development of effective therapeutics. In this review, diagnostic strategies for MBL strains and biochemical analyses of potent small-molecule inhibitors from experimental reports (2020- date) are overviewed. Notably, N1 and N2 from natural sources, S3–S7, S9 and S10 and S13–S16 from synthetic routes displayed the most potent broad-spectrum inhibition with ideal safety profiles. Their mechanisms of action include metal sequestration from and multi-dimensional binding to the MBL active pockets. Presently, some β-lactamase (BL)/MBL inhibitors have reached the clinical trial stage. This synopsis represents a model for future translational studies towards the discovery of effective therapeutics to overcome the challenges of AR.