Parasitological and Bacterial Contamination of Nigerian Currency Notes and the Antimicrobial Resistance of the Isolates in Akure, Southwestern Nigeria
Bibliographic record
Abstract
Money has been reported as an agent enhancing the transmission of parasites eggs/cysts and microbes from one person to another worldwide including Nigeria. This study examined the parasitological and bacterial contamination of Nigerian currency notes and the antimicrobial resistance of the isolates in Akure, southwestern Nigeria. Standard parasitological and microbial methods were followed. Firstly, 160 pieces of various denominations of the Nigerian naira notes were randomly collected aseptically from various sources in Akure. Secondly, sterile cotton swabs moistened with buffered peptone water solution were used for swabbing each naira note and the swabs were separately soaked into 15 mL sterile buffered peptone water solution. Centrifugation was used in order to make parasites eggs/cysts to sediment and examined through the light microscope. Of the 160 samples investigated, 63 (39.4%) were found to be positive for parasite eggs/cysts. Also, ₦100 (80.0%) and ₦1000 (10.0%) significantly ( p <0.05) have the highest and lowest currency denomination parasitic contamination. Moreover, parasites eggs/cysts isolated include eggs/cysts of Enterobius vermicularis (8.9%), Hookworm (4.9%), Entamoeba histolytica (34.5%), Flagellates (5.4%), Ascaris sp . (29%), Strongyloides stercoralis (2.2%), Isospora sp . (3.1%) and Trichuris trichiura (12.9%). Also, average total bacterial load of 2186.9 × 10 3 cfu/mL was recorded for all the currency notes examined ( p <0.05). The major bacterial species isolated from the samples examined are Staphylococcus aureus (23.1%), Escherichia coli (17.2%) and Pseudomonas sp (15.8%). It is apparent from this study that Nigeria Naira note particularly ₦100 can serve as an agent enhancing the transmission of parasites egg/cysts and bacteria in Akure metropolis.
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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 | 0.000 |
| 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".