Transfer of bacteria from fabrics to hands and other fabrics: development and application of a quantitative method using<i>Staphylococcus aureus</i>as a model
Bibliographic record
Abstract
AIMS: To develop and apply a quantitative protocol for assessing the transfer of bacteria from bleached and undyed fabrics of 100% cotton and 50% cotton + 50% polyester (poly cotton) to fingerpads or other pieces of fabric. METHODS AND RESULTS: Test pieces of the fabrics were mounted on custom-made stainless steel carriers to give a surface area of 1 cm in diameter, and each piece seeded with about 10(5) cfu of Staphylococcus aureus from an overnight broth culture; the inoculum contained 5% fetal bovine serum as the soil load. Transfer from fabric to fabric was performed by direct contact using moist and dry fabrics. Transfers from fabrics to fingerpads of adult volunteers were tested using moist, dry and re-moistened pieces of the fabrics, with or without friction during the contact. Bacterial transfer from fabrics to moistened fingerpads was also studied. All the transfers were conducted under ambient conditions at an applied pressure of 0.2 kg cm(-2). After the transfer, the recipient fingerpads or fabric pieces were eluted, the eluates spread-plated, along with appropriate controls, on tryptic soy agar and the percentage transfer calculated after the incubation of the plates for 24 h at 37 degrees C. CONCLUSION: Bacterial transfer from moist donor fabrics using recipients with moisture was always higher than that to and from dry ones. Friction increased the level of transfer from fabrics to fingerpads by as much as fivefold. Bacterial transfer from poly cotton was consistently higher when compared with that from all-cotton material. SIGNIFICANCE AND IMPACT OF THE STUDY: The data generated should help in the development of better models to assess the role fabrics may play as vehicles for infectious agents. Also, the basic design of the reported methodology lends itself to work with other types of 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".