Proof of concept for detection of staphylococcal enterotoxins in platelet concentrates as a novel safety mitigation strategy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Staphylococcus aureus is a predominant contaminant of platelet concentrates (PCs) that can evade detection during screening with culture methods. Importantly, S. aureus produces staphylococcal enterotoxins (SEs) during PC storage, which are linked to slow growth and enhanced biofilm formation. This study investigated timing of SE production during PC storage and feasibility of SE detection as a PC safety strategy. MATERIALS AND METHODS: Genomic and transcriptomic data of transfusion-relevant S. aureus PS/BAC/169/17/W, PS/BAC/317/16/W, CI/BAC/25/13/W and CBS2016-05 were used to determine the presence and differential expression of exotoxin genes in PCs. Trypticase soy broth (TSB) and PCs were inoculated with 1.0E+06 cfu/mL of S. aureus PS/BAC/169/17/W and CBS2016-05. Expression of SEs at different growth phases was confirmed with Western blotting. PCs were inoculated with 30 cfu/unit of the same strains, and SE detection during PC storage was optimized with a sandwich dot-ELISA assay. RESULTS: S. aureus genomes contain multiple exotoxin genes including those encoding for SEs. Transcriptome data revealed significant upregulation (0.5-6.7-fold, p < 0.05) of SE genes in PCs versus TSB. Western blots demonstrated SE production at all growth phases. Notably, dot-ELISA detected clinically relevant concentrations of SEs (~0.2 μg/mL) at 32 h of PC storage when S. aureus PS/BAC/169/17/W and CBS2016-05 counts were 1.8E+04 and 1.4E+04 cfu/mL, respectively. CONCLUSION: Genomic analyses revealed that staphylococcal exotoxins are widely distributed and highly conserved among transfusion-relevant S. aureus isolates. Furthermore, SEs are significantly upregulated in PCs and detected at 30 h of PC storage. Therefore, bacterial toxin detection could supplement mitigation strategies to enhance PC safety.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".