The polytetrafluoroethylene (PTFE) channel model of cyclic-buildup biofilm and traditional biofilm: The impact of friction, and detergent on cleaning and subsequent high-level disinfection
Bibliographic record
Abstract
OBJECTIVE: To evaluate the efficacy of detergent and friction on removal of traditional biofilm and cyclic-buildup biofilm (CBB) from polytetrafluoroethylene (PTFE) channels and to evaluate the efficacy of glutaraldehyde to kill residual bacteria after cleaning. METHODS: PTFE channels were exposed to artificial test soil containing 108 CFU/mL of Pseudomonas aeruginosa and Enterococcus faecalis, followed by full cleaning and high-level disinfection (HLD) for five repeated rounds to establish CBB. For traditional biofilm, the HLD step was omitted. Cleaning with enzymatic and alkaline detergents, bristle brush, and Pull Thru channel cleaner were compared to a water flush only. Carbohydrate, protein, viable count, adenosine triphosphate (ATP) levels were analyzed and atomic force microscopy (AFM) was performed. RESULTS: In the absence of friction, cleaning of traditional biofilm and CBB was not effective compared to the positive control (Dunn-Bonferroni tests; P > .05) regardless of the detergent used. ATP, protein, and carbohydrate analyses were unable to detect traditional biofilm or CBB. The AFM analysis showed that fixation resulted in CBB being smoother and more compact than traditional biofilm. CONCLUSION: Friction during the cleaning process was a critical parameter regardless of the detergent used for removal of either traditional biofilm or CBB. Glutaraldehyde effectively killed the remaining microorganisms regardless of the cleaning method used.
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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 | 0.000 |
| 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 | 0.001 | 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".