The impact of red blood cell manufacturing variables on bacterial growth dynamics: a pilot study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Bacterial contamination of red blood cells (RBC) remains a rare but serious clinical concern. Despite the low temperature storage of RBC, some bacteria can proliferate. The impact of RBC additive solutions (AS), manufacturing method or donor sex on bacterial growth/survival in RBC was addressed in this pilot study. MATERIALS AND METHODS: Using a partial pool-and-split design, bacterial growth/survival was assessed in intentionally inoculated RBC, manufactured separately from male and female donors using three different manufacturing methods (two whole blood [WB] filtration methods; one RBC filtration method), and resuspended in one of four AS: SAGM, PAGGSM, AS-1 or AS-3. At the beginning of storage, RBC were inoculated with 10 CFU/ml of either Klebsiella pneumoniae, Staphylococcus epidermidis, Yersinia enterocolitica or Propionibacterium acnes. Manufacturing, inoculation, storage (until day 42) and monitoring of bacterial growth were conducted at two sites: Canadian Blood Services and Héma-Québec. RESULTS: Yersinia enterocolitica was the only bacterium that proliferated during storage at both sites. RBC tested at Canadian Blood Services had higher bacterial concentrations than those at Héma-Québec (P = 0·0044). At Héma-Québec, where two different manufacturing methods were used, Y. enterocolitica reached significantly higher bacterial concentrations in AS-3 RBC (WB filtration method) compared to units prepared in the other three AS (RBC filtration method; P < 0·05). Bacterial survival/growth dependent on donor sex was not uniformly noted. CONCLUSION: Only one of four bacteria grew under RBC storage conditions. The results indicate that RBC manufacturing variables, rather than AS or donor sex, affect bacterial growth in RBC.
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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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".