Bacteriological Evaluation Of Nigerian Currency Notes From Selected Handlers In Ilesha Metropolis Of Osun State, Nigeria
Bibliographic record
Abstract
Background: Peoples from various background and from difffferent works of life with difffferent hygienic status always engaged in physical transactions with a legal tender of varied denominations for exchange of goods and services, and one of the legal tender offiffifficially recognized in Nigeria is called naira. This study evaluated bacterial contaminants onnaira notes in circulations from selected handlers with specifific profession and determined the resistance of the isolates to conventional antibiotic in use. Methods: A total of 160 samples of currency notes 20 each of 8 existing denominations in Nigeria, collected from selected participants of various professions soaked in ringer's solution were serially diluted, subculture to various bacteriological media, Gram stained and biochemically characterized. Determination by antibiogram study was carried out,with a view to identifying resistance risk factors that could be associated with these contaminatedcurrency notes. Results: The microbial load was found to be higher in lower denominations irrespective of their polymer status. The total bacterial count per milliliter varied between 2.28 ×104 and 4.20×107 CFU, while the percentage distributions of isolates; Staphylococcus aureus (36.8%), Escherichia coli (31.5%), Bacillus spp (3.7%) and Pseudomonas aeruginosa (27.5%) and varied resistance to antibiotics used were recorded. Conclusion: Bacterial antibiotic resistance has been associated with treatment failure, high health cost burden and loss of manpower hours due to over hospitalization. The microbial contaminant loads capable of causing opportunistic infection were found to be present in currency notes examined. The alarming resistance of bacteria to selected conventional antibiotics used in this study, serves an indication of potential threat of contaminated currency notes to public health.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".