Occurrence of Antibiotic Residues and Antibiotic-Resistant Bacteria in Nile Tilapia Sold in Some Markets in Accra, Ghana: Public Health Implication
Bibliographic record
Abstract
In Ghana there are concerns that antibiotics may be used inappropriately to boost fish production, though no study has investigated this problem. To provide preliminary insights into public health aspects of the problem, we investigated the occurrence of antibiotic residues and antibiotic-resistant bacteria in Nile tilapia (Oreochromis niloticus), a fish commonly cultivated and consumed in Ghana. Two hundred Nile Tilapia fish were randomly sampled from four major markets in Accra, the capital city of Ghana. One hundred samples were screened for antibiotic residues using a microbial inhibition plate test that detects sixteen different antibiotics commonly used in animal husbandry and aquaculture. The other 100 samples were cultured for bacteria using direct culture methods, and the isolates were tested against seven antibiotics by the Kirby Bauer method. The overall prevalence of antibiotic residues in the fish samples was 7%. Bacteria that were isolated from the fish samples were Shigella sonnei (10%), Enterobacter cloacae (7%), Escherichia coli (6%), Salmonella Typhi (3%) and Klebsiella pneumoniae and Proteus mirabilis (2%). All bacteria isolated were susceptible to gentamicin and ciprofloxacin but resistant to ampicillin. Multi-drug resistance (ie resistance to three or more different classes of antibiotics) occurred in 86.7% of the isolates. Nile Tilapia sold in Accra is a source of multi-drug resistant bacteria. Consumption of the fish can also lead to significant exposure to antibiotic residues.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".