Bacterial Contamination of Iranian Paper Currency.
Bibliographic record
Abstract
BACKGROUND: Transmission of human pathogens can be occurred via inert objects. Paper currency is a further common contact surface whereby pathogens can be transferred within a population although the significance remains unknown. Hence, the aim of the present study was to investigate microbial populations associated with Iranian paper currency. METHODS: This study was carried out by getting 108 samples of the Iranian currency notes (1000, 2000, 5000, 10000, 20000 and 50000 RIALS) from food-related shops that included food service outlets, greengrocery, supermarket, bakery, confectionary and poultry meat retail outlets. All currency notes were examined for total bacterial count and identification of pathogenic bacteria. RESULTS: The average total bacterial count that was recovered from currency notes was found to be 3.27±0.31 colony forming unites.2000R had the highest total bacterial count, followed by 5000R, 10000R and the lowest in 50000R. In this study, the isolated bacteria recovered were Bacillus cereus (8.33%), E. coli (48.14%), Staphylococcus aureus(28.7%), Salmonella (0.92%), Listeria monocytogenes (0.92%), Yersinia entrocolitica(6.48%). It was revealed that all the pathogens screened for where encountered on currency notes were recovered from one sample. There were no significant (P>0.05) correlations between the carriage of pathogens/fecal indicator bacteria and currency note condition. CONCLUSION: Our findings demonstrate that Iranian currency notes represent a significant vehicle for human 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".