Genomic Characterisation of Multidrug-Resistant Pathogenic Enteric Bacteria from healthy children in Osun State, Nigeria
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) has been established to be a significant driver for the persistence and spread of bacterial infections. It is, therefore, essential to conduct epidemiological surveillance of AMR in healthy individuals to understand the actual dynamics of AMR in Nigeria. Multi-drug resistant Klebsiella quasivariicola (n=1) , Enterobacter hormaechei (n=1), and Escherichia coli (n=3) from stool samples of healthy children were subjected to whole genome sequencing using Illumina Nextseq1000/2000 and Oxford nanopore. Bioinformatics analysis reveals antimicrobial resistance, virulence genes, and plasmids. This pathogenic enteric bacteria harbored more than three plasmid replicons of either Col and/or Inc type associated with outbreaks and AMR resistant gene pmrB responsible for colistin resistance. Plasmid reconstruction revealed an integrated tetA gene responsible for tetracycline resistance, and caa gene responsible for toxin production in two of the E.coli isolates, and a cusC gene known to induce neonatal meningitis in the K. quasivariicola ST3879. The global spread of MDR pathogenic enteric bacteria is a worrying phenomenon, and close surveillance of healthy individuals, especially children, is strongly recommended to prevent the continuous spread and achieve the elimination and eradication of these infections. Molecular epidemiological surveillance using whole genome sequencing (WGS) will improve the detection of MDR pathogens in Nigeria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".