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Biomedical subjects

Rea Maja Kobialka

Publications and source records attributed to Rea Maja Kobialka.

2 recordsLinked to original sources

Comparative performance of portable DNA extraction protocols and bioinformatics workflows for rapid detection of gram-negative bacteria and antimicrobial resistance using Oxford Nanopore sequencing.

Oxford Nanopore Technology (ONT) enables rapid, portable pathogen identification and antimicrobial resistance (AMR) detection, but the reliability of downstream genomic analyses is highly dependent on DNA extraction quality, particularly in resource-limited settings. This study comparatively evaluated four portable bacterial DNA extraction protocols derived from three commercial kits to determine their impact on nanopore sequencing performance, bioinformatics workflow completion, and field deployability. Six gram-negative bacterial isolates (Escherichia coli, n = 4; Pseudomonas sp., n = 1; and Salmonella sp., n = 1) were processed using four extraction protocols: SwiftX DNA, SwiftX DNA with proteinase K (ProtK), SwiftX ParaBact, and NucleoSpin Microbial. Twenty-four resulting DNA extracts were sequenced on a single multiplexed MinION R10.4.1 flow cell. Sequencing data were analyzed using validated Galaxy-based generic and species-specific pipelines. Workflow completion was defined as successful progression through quality control, assembly, virulence, plasmid, and AMR detection modules. DNA purity varied substantially by extraction protocol and was strongly associated with successful workflow completion (Kruskal-Wallis, P = 0.0006). Accordingly, NucleoSpin Microbial achieved 100% workflow completion, and SwiftX ParaBact achieved 83%, while both SwiftX DNA-based protocols failed to complete full workflows. Importantly, key AMR genes required to classify isolates as multidrug-resistant were consistently detected using both NucleoSpin Microbial and SwiftX ParaBact extractions. However, NucleoSpin Microbial assemblies showed significantly higher contiguity and enabled a broader, more complete detection of virulence factors, pathogenicity islands, plasmid replicons, and accessory AMR genes, reflecting enhanced genomic resolution.IMPORTANCERapid whole-genome sequencing is increasingly used to detect antimicrobial resistance and guide public health responses, but its reliability depends strongly on how bacterial DNA is extracted. In this study, we have shown that DNA extraction method choice has a major impact on Oxford Nanopore sequencing performance across clinically relevant gram-negative bacteria. While silica column-based extraction maximized genomic completeness and analytical depth, paramagnetic bead-based reverse purification offered superior portability with sufficient resolution for frontline AMR surveillance. These findings highlight a practical trade-off between field deployability and high-resolution genomic characterization in low-resource settings.

DNA extraction

Artificial intelligence in molecular diagnostics for pandemic preparedness.

INTRODUCTION: Molecular diagnostics focusing on the detection and analysis of nucleic acids are indispensable tools for early pathogen identification, transmission monitoring, and genomic surveillance during pandemics. Recent technological advances have broadened the diagnostic landscape, incorporating PCR-based methods, isothermal amplification, high-CRISPR-based amplification detection, and sequencing. Despite their diagnostic potential, widespread implementation remains limited by high validation costs, time and logistical constraints, the need for specialized professional knowledge, and a lack of adaptability in resource-limited settings. Artificial intelligence (AI) is increasingly recognized as a promising but challenging approach, offering tools that streamline assay development, automate data interpretation, and optimize real-time diagnostic performance. AREAS COVERED: This review introduces recently published AI tools with potential to enhance the in-silico design validation process of oligonucleotides for molecular assays. These cover tools for initial assay design and optimization to validation and continuous assay updates. The limitations, including concerns regarding data accuracy, the lack of transparency in data processing ('black box' models), and unresolved licensing and regulatory issues, are highlighted for each tool and as expert opinion. EXPERT OPINION: Collectively, these challenges currently confine most AI-based approaches to research settings and prevent their routine implementation in clinical molecular diagnostics. Their widespread adoption depends on addressing remaining technical, regulatory, and practical challenges.

Humans