<i>Cutibacterium acnes</i> contamination does not enhance the proinflammatory profile of platelet concentrates
Bibliographic record
Abstract
BACKGROUND: Cutibacterium acnes, a common anaerobic platelet concentrate (PC) contaminant, has been associated with rare mild adverse transfusion reactions and is often considered a harmless commensal. Notably, C. acnes can cause chronic infections and has been shown to induce the release of proinflammatory cytokines by immune cells. Since elevated concentrations of proinflammatory factors in PCs have been linked to noninfectious adverse reactions, this study aimed to assess whether C. acnes could elicit the release and accumulation of proinflammatory factors during PC storage, thereby enhancing the risk of such reactions. STUDY DESIGN/METHODS: Four ABO-matched buffy coat PCs were pooled and split into six units, each were inoculated with either saline (negative control), a Staphylococcus aureus isolate (positive control, 30 colony forming units [CFU]/unit), or four C. acnes PC isolates (10 CFU/mL) and stored at 20-24°C with agitation. Bacterial counts, platelet activation, and concentration of proinflammatory factors were assessed on days 0, 3, and 5. N = 3. RESULTS: CFU/mL by the end of PC storage. By day 5, no significant differences in platelet activation or proinflammatory cytokine profiles were observed in C. acnes-contaminated PCs compared to the negative control (p > .05), while there was a significant increase (p ≤ .05) in sCD40L concentration (day 3), and platelet activation and IL-8 concentration (day 5) in S. aureus-contaminated units. DISCUSSION: C. acnes contamination does not promote the accumulation of proinflammatory factors in the absence of proliferation during storage and may not enhance the risk of inflammatory reactions when transfused to patients.
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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 | 0.001 |
| 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 | 0.001 | 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".