Efficiency of riboflavin and ultraviolet light treatment against high levels of biofilm‐derived <i>Staphylococcus epidermidis</i> in buffy coat platelet concentrates
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Staphylococcus epidermidis forms surface-attached aggregates (biofilms) in platelet concentrates (PCs), which are linked to missed detection during PC screening. This study was aimed at evaluating the efficacy of riboflavin-UV treatment to inactivate S. epidermidis biofilms in buffy coat (BC) PCs. MATERIALS AND METHODS: colony-forming units (CFU)/ml) (N = 4-5). One spiked and three unspiked WB units were processed to produce a BC-PC pool. Riboflavin was added to the pool which was then split into two bags: one for UV treatment and the second was untreated. Bacterial counts were determined before and after treatment. In vitro PC quality was assessed by flow cytometry and dynamic light scattering. RESULTS: CFU/ml in PCs (P < 0·0001). Riboflavin-UV treatment resulted in significantly higher reduction of S. epidermidis AZ-66 than strain ST-10002 (≥3·5 log reduction and 2·6-2·8 log reduction, respectively, P < 0·0001). Remaining bacteria post-treatment were able to proliferate in PCs. No differences in S. epidermidis inactivation were observed in PCs produced from WB inoculated with biofilm or non-biofilm cells (P > 0·05). Platelet activation was enhanced in PCs produced with WB inoculated with biofilms compared to non-biofilm cells (P < 0·05). CONCLUSION: CFU/ml were not completely inactivated; however, further testing is necessary with lower (real-life) bacterial levels.
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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 | 0.000 | 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 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".