Unveiling the hidden threat: whole-genome sequencing of Ochrobactrum anthropi and Empedobacter brevis from vended foods and naira notes in Southwest Nigeria—implications for food safety and virulence
Bibliographic record
Abstract
Abstract Background Foodborne illnesses remain a pressing public health issue, particularly in developing countries such as Nigeria, where food vendors often operate without adequate regulatory oversight. This study investigated the prevalence of Ochrobactrum anthropi and Empedobacter brevis in food samples and circulating Naira notes within Ondo State, Nigeria. Methods A total of 462 samples, including various food matrices and Naira notes, were collected from urban areas in the three Senatorial districts of Ondo State, Southwest Nigeria. Whole-genome sequencing (WGS) was employed to analyse the genetic composition, virulence factors, and antimicrobial resistance profiles of the bacterial strains. Results O. anthropi had a genome size of 20.57 Mb with 21,664 coding sequences (CDSs), whereas E. brevis presented significant genomic contiguity, with a contig N50 of 1005 kb. The aminoglycoside resistance genes gidB and ANT(6)-1 (Empedobacter brevis NZ_CP013210.1) aadK and ANT(6)-1 (Ochrobactrum anthropi ATCC 49188) were mostly detected. Ochrobactrum anthropi ATCC 49188) encodes virulent metabolic and stress response genes (alkA, ansC, aroC, and arsR6), amino acid and protein synthesis genes (aspB and aspC) and uncharacterized novel genes (BMEI0066, BMEI0085, and BMEI1809). Conclusions The presence of these potential bacterial pathogens in food samples and Naira notes underscore the critical intersection between food safety practices and microbial contamination, which emphasizes the urgent need for improved food safety protocols in Ondo State. Targeted interventions are recommended to mitigate the risks associated with these virulent pathogens, particularly through enhanced surveillance and education for food handlers and consumers, which is effective in combatting foodborne diseases linked to antimicrobial-resistant pathogens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".