Bacterial survival and distribution during buffy coat platelet production
Bibliographic record
Abstract
Background and Objectives At Canadian Blood Services, buffy coat (BC) platelet concentrates (BC‐PCs) show a generally lower bacterial contamination rate than apheresis PCs. This study investigated whether the PC production method contributes to this observation. Materials and Methods Whole blood (WB) inoculated with eight bacterial strains was processed using the BC method. Bacteria were enumerated throughout BC‐PC production and subsequent PC storage. Endotoxin production and bacterial adhesion to PC bags were evaluated during PC storage. PC quality was monitored by CD62P expression (flow cytometry) and changes in dynamic light scattering (ThromboLUX®). Results During overnight WB hold, Staphylococcus epidermidis titres remained unchanged, commercial Escherichia coli and Klebsiella pneumoniae were eliminated and the remaining organisms proliferated to high concentrations. Through BC‐PC production, bacteria segregated preferentially towards the cellular fractions compared to plasma (P < 0·05). During PC storage, most bacteria adhered to the PC bags and Gram negatives produced clinically significant endotoxin levels. Changes in CD62P expression or ThromboLUX scoring did not consistently reflect bacterial contamination in BC‐PCs. Conclusion WB hold during BC‐PC production does not have a broad‐spectrum bactericidal effect, and therefore, other factors contribute to low rates of contamination in BC‐PCs.
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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".