Establishment of transfusion‐relevant bacteria reference strains for red blood cells
Bibliographic record
Abstract
Abstract Background and objectives Red blood cell concentrates (RBCC) are susceptible to bacterial contamination despite cold storage. A reliable evaluation of strategies to minimize the risk of RBCC‐associated bacterial transmission requires the use of suitable reference bacteria. Already existing Transfusion‐Relevant Bacteria Reference Strains (TRBRS) for platelet concentrates fail to grow in RBCC. Consequently, the ISBT TTID, Working Party, Bacterial Subgroup, conducted an international study on TRBRS for RBCC. Materials and methods Six bacterial strains ( Listeria monocytogenes PEI‐A‐199, Serratia liquefaciens PEI‐A‐184, Serratia marcescens PEI‐B‐P‐56, Pseudomonas fluorescens PEI‐B‐P‐77, Yersinia enterocolitica PEI‐A‐105, Yersinia enterocolitica PEI‐A‐176) were distributed to 15 laboratories worldwide for enumeration, identification, and determination of growth kinetics in RBCC at days 7, 14, 21, 28, 35 and 42 of storage after low‐count spiking (10–25 CFU/RBCC). Results Bacterial proliferation in RBCC was obtained for most strains, except for S. marcescens , which grew only at 4 of 15 laboratories. S. liquefaciens , S. marcescens , P. fluorescens and the two Y. enterocolitica strains reached the stationary phase between days 14 and 21 of RBCC storage with a bacterial concentration of approximately 10 9 CFU/ml. L. monocytogenes displayed slower growth kinetics reaching 10 6 –10 7 CFU/ml after 42 days. Conclusion The results illustrate the importance of conducting comprehensive studies to establish well‐characterized reference strains, which can be a tool to assess strategies and methods used to ameliorate blood safety. The WHO Expert Committee on Biological Standardization adopted the five successful strains as official RBCC reference strains. Our study also highlights the relevance of visual inspection to interdict contaminated RBC units.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 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".