Influence of surface and cloth characteristics on mechanical removal of meticillin-resistant Staphylococcus aureus (MRSA) attached to inanimate environmental surfaces in hospital and healthcare facilities
Bibliographic record
Abstract
In health care facilities, the inanimate environmental surfaces can become contaminated with nosocomial pathogen agents. Cleaning and sanitization has already been accepted as an important and integral factor for controlling the transfer and spread of contaminants. With the advent of new cleaning utilities, there is a need to evaluate the benefits of adapting/ implementing new versus conventional utilities, both in terms of efficacy and cost. For instance, cotton and microfiber cleaning cloths (wipes/towelettes) may help in the removal of soils and attached bacterial cells. In this context, it is also important to know if the nature of the substratum surface can affect the cleaning output when we use both microfiber and cotton cloths. In this work, the role of physicochemical factors on bacterial adhesion to three inanimate/environmental surfaces, stainless steel, melamine and Formica laminate, was investigated by assessing the hydrophobicity by contact angle measurement method. In addition, the topography of the substratum surface and the attachment capability of meticillin-resistant Staphylococcus aureus (MRSA) to the surfaces were studied using scanning electron microscopy (SEM). The results obtained revealed that, with the exception of Formica laminate, MRSA cells could attach to stainless steel and melamine surfaces after short contact time of 24 hours. The outcome also indicated that in presence of soil/ organic matter, the microfiber cloths were only slightly more efficient for the removal of soil and attached microbial cells than the cotton cloths. However, for surfaces without soils, no significant difference was found when cotton cloths or microfiber cloths were used. It was also observed that regardless of the type of cloth material used or the presence of soil, the melamine surfaces were the most difficult ones to clean.
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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 | 0.000 | 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".