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All Journal African Journal of Sciences and Traditional Medicine Open Access DRIVERset African Journal of Medicine, Surgery and Public Health Research
Abhadionmhen, Abel Onolunosen
Unknown Affiliation
Biochemistry, Genetics & Molecular Biology Chemistry Health Professions Immunology & microbiology Medicine & Pharmacology Nursing Public Health

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Journal : Open Access DRIVERset

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Enhancing Water Health Monitoring with ML Techniques for Detection of Coliform Bacteria: A Review Abhadionmhen, Abel Onolunosen; Abhadiomhen, Stanley Ebhohimhen
African Journal of Sciences and Traditional Medicine Vol 1 No 1 (2024): African Journal of Sciences and Traditional Medicine
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstm.v1i1.3690

Abstract

Water health monitoring is critical for ensuring safe drinking water and preventing waterborne diseases. Traditional methods for detecting coliform bacteria, including culture-based techniques and biochemical tests, are well-established but face limitations such as time consumption, high costs, and labor intensity, particularly in resource-limited settings like Nigeria. Recent cholera outbreaks in Nigeria have underscored the urgent need for more effective and timely water quality monitoring solutions. This review explores the application of machine learning (ML) techniques in enhancing the detection of coliform bacteria, offering a promising alternative to traditional methods. ML approaches, including Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), and Ensemble Methods, are evaluated for their potential to provide faster, more accurate, and scalable detection of coliform contamination. The review highlights key challenges, such as data quality, computational demands, and infrastructure limitations, and discusses real-world case studies demonstrating the practical applications and limitations of ML techniques. The integration of ML models into water monitoring systems shows considerable promise but requires addressing critical issues related to data quality and model feasibility in low-resource settings. Future research directions include exploring hybrid systems that combine ML with traditional methods, leveraging emerging technologies like edge computing, and enhancing model robustness through innovative data strategies. By advancing the application of ML in water health monitoring, it is possible to improve public health outcomes and effectively manage waterborne diseases.
Genomic Insights into Antimicrobial Resistance in Salmonella typhi: A Bioinformatics-Based Surveillance Model from Public Datasets with Implications for Resource-Limited Settings Usiabulu, Ehizokhale Jude; Abhadionmhen, Abel Onolunosen; Iduku, Husseni
African Journal of Sciences and Traditional Medicine Vol 2 No 2 (2025): African Journal of Sciences and Traditional Medicine
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstm.v2i2.6485

Abstract

Antimicrobial resistance (AMR) in Salmonella typhi represents an escalating global health challenge, particularly in regions with limited capacity for surveillance and treatment. This study investigates the genetic diversity and AMR mechanisms of S. typhi strains using publicly available genomic data. Twenty genomes were retrieved from GenBank and analyzed to identify resistance genes and genetic variations. The analysis focused on key AMR determinants, including blaTEM (beta-lactam resistance), qnrS (quinolone resistance), and aac(3)-I (aminoglycoside resistance), assessing their distribution across isolates. Phylogenetic analysis revealed substantial genetic diversity and indicated clonal dissemination of strains with similar resistance profiles. Mutation screening of gyrA and parC genes associated with fluoroquinolone resistance identified recurrent mutations, underscoring their role in resistance development. Bioinformatics tools such as BLAST+, Prokka, ResFinder, and iTOL were employed for sequence alignment, gene annotation, AMR gene detection, and phylogenetic reconstruction. The findings demonstrate the effectiveness of bioinformatics approaches in AMR surveillance, especially in resource-constrained settings where direct sample collection is often impractical. This study highlights the pervasive presence of AMR genes in S. typhi and reinforces the value of genomic surveillance in tracking resistance trends and informing targeted public health interventions. The research offers a novel and efficient model for AMR monitoring and provides foundational insights into resistance mechanisms in S. typhi, with implications for regions affected by AMR, such as Northeast Nigeria.
Co-Authors Abhadiomhen, Stanley Ebhohimhen Iduku, Husseni Usiabulu, Ehizokhale Jude