Biofilm‐forming skin microflora bacteria are resistant to the bactericidal action of disinfectants used during blood donation
Bibliographic record
Abstract
BACKGROUND: A one-step skin disinfection method containing 2% chlorhexidine-gluconate (CHG) and 70% isopropyl alcohol (IPA) is currently used by blood suppliers worldwide. Reports of bacterially contaminated platelet concentrates (PCs) indicate that skin disinfection is not fully effective. Approximately 20% of skin microflora exist as surface-attached aggregates (biofilms), known for displaying increased resistance to disinfectants. This study was aimed at determining whether skin microflora biofilm-positive Staphylococcus epidermidis and Staphylococcus capitis are resistant to CHG and/or IPA. STUDY DESIGN AND METHODS: Free-floating cells and mono or dual (1 : 1 ratio) biofilms of S. epidermidis and S. capitis were exposed to CHG, IPA, or CHG/IPA for 30 seconds, simulating skin disinfection practices. Residual viable cells were quantified by colony counting. Morphology of disinfectant-treated S. epidermidis biofilms was examined by scanning electron microscopy. Treated S. epidermidis and S. capitis biofilms were inoculated into PCs and bacterial concentrations were determined on Days 0 and 5 of storage. RESULTS: Treatment of staphylococcal biofilm cells with all disinfectants caused cell damage and significant reduction in viability, with CHG/IPA being the most effective. However, biofilms were significantly more resistant to treatment than free-floating cells. Disinfectant-treated S. epidermidis proliferated better in PCs than S. capitis, especially when grown as monospecies biofilms. CONCLUSION: Although CHG/IPA is effective in reducing the viability of S. epidermidis and S. capitis biofilms, these organisms are not completely eliminated. Furthermore, disinfectant-treated staphylococcal biofilms multiply well in PCs. These results demonstrate that the biofilm-forming capability of the skin microflora reduces the bactericidal efficiency of blood donor skin disinfectants.
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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".