Comparable bacterial growth in platelet concentrates suspended in plasma and platelet additive solution and improved detection of bacterial contamination using a new generation automated culture system
Bibliographic record
Abstract
BACKGROUND: Microbial screening of platelet concentrates (PC) with automated culture methods is widely implemented to reduce septic transfusion reactions. Herein, detection of bacterial contamination in PC was compared between units prepared in plasma and a mix of plasma and platelet additive solution (PAS) and between the BACT/ALERT 3D and next generation BACT/ALERT VIRTUO systems. STUDY DESIGN/METHODS: Double apheresis units were split into single units, diluted in either PAS (PAS-PC) or plasma (plasma-PC), and tested for in vitro quality and sterility prior to spiking with ~30 CFU/unit of Staphylococcus epidermidis, Staphylococcus aureus, Serratia marcescens, and Klebsiella pneumoniae or ~10 CFU/mL of Cutibacterium acnes. Spiked PC were sampled for BACT/ALERT testing (36 and 48 h post-spiking) and colony counts (24, 36, and 48 h post-spiking). Times to detection (TtoD) and bacterial loads were compared between PC products and BACT/ALERT systems (N = 3). RESULTS: Bacterial growth was similar in plasma-PC and PAS-PC. No significant differences in TtoD were observed between plasma-PC and PAS-PC at the 36-h sampling time except for S. epidermidis which grew faster in plasma-PC and C. acnes which was detected earlier in PAS-PC (p < .05). Detection of facultative bacteria was 1.3-2.2 h sooner in VIRTUO compared with 3D (p < .05) while TtoD for C. acnes was not significantly different between the two systems. DISCUSSION: Comparable bacterial detection was observed in plasma-PC and PAS-PC with PC sampling performed at 36-h post blood collection. PC sampling at ≤36 h could result in faster detection of facultative pathogenic organisms with the VIRTUO system and improved 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".