ONE HEALTH EVALUATION OF ANTIMICROBIAL RESIDUES AND RESISTANCE IN ANIMAL-SOURCE FOODS AND THEIR IMPLICATIONS ON NUTRITIONAL STATUS IN SOKOTO STATE, NIGERIA
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a major global public health challenge closely linked to antimicrobial use in food-producing animals. In Sokoto State, Nigeria, animal-source foods (ASFs) are essential for nutrition but may also serve as vehicles for antimicrobial residues and resistant organisms, with potential implications for food safety and nutritional well-being. This systematic review synthesized published and grey literature to identify, appraise, and summarize evidence on antimicrobial residues and resistant bacteria in ASFs in Sokoto State. Electronic databases including PubMed, Scopus, Web of Science, African Journals Online, and Google Scholar were searched, complemented by grey literature, for studies published between January 2000 and March 2025. Eligible studies reported antimicrobial residues or antimicrobial resistance in meat, milk, eggs, or other ASFs. Data extraction followed PRISMA 2020 guidelines, and study quality was assessed using the Newcastle–Ottawa Scale and Cochrane RoB 2.0 tool. Due to substantial methodological heterogeneity, findings were synthesized narratively. Forty-five studies (38 peer-reviewed and 7 grey literature) met the inclusion criteria. Tetracyclines and β-lactams were the most frequently detected antimicrobial residues, with concentrations often exceeding Codex Alimentarius–recommended maximum residue limits. Residue prevalence ranged from 18–94% in meat and 22–89% in milk. Antimicrobial-resistant Escherichia coli, Staphylococcus aureus, and Salmonella spp. were commonly isolated, showing high resistance to tetracycline, ampicillin, and cotrimoxazole, with multidrug resistance frequently reported. Overall, the findings indicate widespread antimicrobial contamination and resistance in ASFs in Sokoto State, underscoring the need for strengthened surveillance, regulatory oversight, and antimicrobial stewardship within a One Health framework
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".