In vivo assessment of antimicrobial-treated textiles on skin microflora
Bibliographic record
Abstract
Purpose – Antimicrobials may be incorporated into garments to protect the textiles, control malodour or to potentially reduce the spread of infection. Yet still not well understood is how antimicrobial-treated textiles may influence a person's resident microflora during wear, as limited in vivo testing has previously been carried out. The purpose of this paper is to assess whether normal skin microflora was altered as a result of contact with selected antimicrobial-treated fabrics. Design/methodology/approach – Three selected antimicrobial-treated fabrics (i.e. Fabric 1: triclosan; Fabric 2: zinc pyrithione derivative; and Fabric 3: silver chloride and titanium dioxide) were placed on the forearm of participants ( n =19). Bacterial counts obtained under treated and untreated fabrics following 24 hours of occlusion were compared. The antimicrobial efficacy of fabrics displayed in vitro was also compared with the activity displayed in vivo . Findings – Two of the three fabrics (Fabrics 1 and 2) reduced bacterial populations on the skin following 24 hours occlusion compared to the matched control fabrics (Fabric 1: p <0.05; Fabric 2: p <0.001). Whereas, following occlusion with Fabric 3 bacterial populations were not significantly different than the matched control. The present study demonstrated that in vitro assessment of antimicrobial capacities of fabrics do not necessarily predict the effects of such fabrics during wear. Originality/value – The paper highlights that in vivo studies are a necessary and important tool for understanding the interactions of an antimicrobial-treated fabric with the wearer's skin. As well, the new method developed can be used by other researchers to examine the potential impact on skin microflora due to contact with antimicrobial-treated textiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".