Comparative characterisation of the biofilm-production abilities of Staphylococcus epidermidis isolated from human skin and platelet concentrates
Bibliographic record
Abstract
PURPOSE: Staphylococcus epidermidis is the predominant contaminant of platelet concentrates (PCs), a blood product used to treat patients with platelet deficiencies. This microorganism is able to form surface-attached aggregates (biofilms) in human skin. Herein, the abundance of S. epidermidis biofilm-producers in contaminated PCs compared to skin isolates was explored. Furthermore, the potential positive selection of S. epidermidis biofilm-producers during the blood donation process and PC manufacturing was investigated. METHODOLOGY: Twenty-four S. epidermidis isolates obtained from contaminated PCs and 48 S. epidermidis isolates obtained from the venipuncture area of human volunteers were compared for their ability to form biofilms in laboratory media and in PCs using a semi quantitative crystal violet assay. Also, the presence of the biofilm-associated icaA and icaD genes was assessed by PCR-amplification.Results/Key findings.Biofilm production in laboratory media showed a higher number of S. epidermidis biofilm-producers in the skin-derived group (43.7 %) compared to the PC-derived isolates (25 %). However, all skin and PC isolates formed biofilms in PCs. The prevalence of ica-positive biofilm-producer isolates was similar in PC and skin isolates (16.6 and 18.8 %, respectively). In contrast, the abundance of ica-negative biofilm-producers was lower in PC isolates compared to skin isolates (8.3 vs 25 %, respectively). CONCLUSION: Positive selection of S. epidermidis biofilm-producers during blood donation and PC manufacturing was not observed. Interestingly, ica-negative biofilm-producers seem to be negatively affected by skin disinfection, blood processing and PC storage. Furthermore, this study shows that S. epidermidis adopts a biofilm-forming phenotype in PCs regardless of its genetic background or origin.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".