Comparison and Efficacy of Breast Pump Cleaning Techniques for Bioburden Reduction
Bibliographic record
Abstract
Background: Donor milk is a good alternative for premature babies whose mothers cannot breastfeed. To reduce the risk of milk contamination, donors have to follow some hygiene instructions, including disinfecting their breast pump (BP). This study aims to investigate the efficacy of BP cleaning and disinfection methods. Methods: Contamination of BP parts was performed by passing milk inoculated with Bacillus cereus , Staphylococcus aureus , or Escherichia coli , through BPs. Devices were then rinsed with cold water or cleaned with hot soapy water. Disinfection was achieved using either a microwave or by immersing BP parts in boiling water. After treatment, residual bacteria were recovered by passing sterile phosphate buffer saline (PBS) through BPs before being inoculated on plates and performing bacterial counts. Method efficiency was assessed by comparing BP residual bioburden to results obtained from BPs that have not undergone cleaning or disinfection treatment (controls). Results: Rinsing BP parts with cold water leads to a diminution of residual bacteria in PBS recovered from device. This decrease is even more effective when hot soapy water is used. There is a slight persistence of all bacteria if disinfection of BPs is performed by using a microwave. This persistence reached up to 3.58 colony-forming unit/mL of sporulating B. cereus in PBS eluted from the pump parts. The use of boiling water, with or without cleaning step, removes bacteria to a level such that no residual contamination was observed. Conclusions: Cleaning BP parts in hot soapy water followed by a disinfection in boiling water ensures a completed decontamination of the BP. These results give evidences for instructions to milk bank donors for whom reducing risks of infections to minimal level is essential.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".