Effect of platelet additive solution on bacterial dynamics and their influence on platelet quality in stored platelet concentrates
Bibliographic record
Abstract
BACKGROUND: Platelet additive solutions (PASs) are an alternative to plasma for the storage of platelet concentrates (PCs). However, little is known about the effect of PAS on the growth dynamics of contaminant bacteria. Conversely, there have been no studies on the influence of bacteria on platelet (PLT) quality indicators when suspended in PAS. STUDY DESIGN AND METHODS: Eight buffy coats were pooled, split, and processed into PCs suspended in either plasma or PAS (SSP+, MacoPharma). PCs were inoculated with 10 and 100 colony-forming units (CFUs)/bag of either Serratia liquefaciens or Staphylococcus epidermidis. Bacterial growth was measured over 5 days by colony counts and bacterial biofilm formation was assayed by scanning electron microscopy and crystal violet staining. Concurrently, PLT markers were measured by an assay panel and flow cytometry. RESULTS: S. liquefaciens exhibited an apparent slower doubling time in plasma-suspended PCs (plasma-PCs). Biofilm formation by S. liquefaciens and S. epidermidis was significantly greater in PCs stored in plasma than in PAS. Although S. liquefaciens altered several PLT quality markers by Days 3 to 4 postinoculation in both PAS- and plasma-PCs, S. epidermidis contamination did not produce measurable PLT changes. CONCLUSIONS: S. liquefaciens can be detected more quickly in PAS-suspended PCs (PAS-PCs) than in plasma-PCs by colony counting. Furthermore, reduced biofilm formation by S. liquefaciens and S. epidermidis during storage in PAS-PCs increases bacteria availability for sampling detection. Culture-based detection remains the earliest indicator of bacterial presence in PAS-PCs, while changes of PLT quality can herald S. liquefaciens contamination when in excess of 10(8) CFUs/mL.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".