Optimization of cord blood unit sterility testing: impact of dilution, analysis delay, and inhibitory substances
Bibliographic record
Abstract
BACKGROUND: Different methods are used by cord blood banks to prepare samples for sterility testing. Suboptimal methods can result in the release of contaminated products. In our organization, samples are prepared by diluting the final product in RPMI-1640 medium. In this work, we have compared our method with different approaches to verify whether optimization should be sought. STUDY DESIGN AND METHODS: Cord blood units (n = 6 units per bacterial strain) characterized to contain inhibitory substances or not were inoculated (10 colony-forming units/mL) with Streptococcus agalactiae, Staphylococcus epidermidis, Klebsiella pneumoniae, Escherichia coli, or Bacteroides fragilis. After plasma and red blood cell removal, stem cell concentrates were diluted in RPMI-1640, thioglycollate, or the unit's plasma. These products, as well as final product, plasma, and red blood cell fractions, were held from 0 to 72 hours at 20 to 24°C before inoculation in culture bottles and detection using the BacT/ALERT 3D system. RESULTS: Dilution of cell concentrates in RPMI-1640 allowed bacterial detection in 93.3% of noninhibitory cord blood samples after a 24-hour storage period. Thioglycollate medium better promoted bacterial growth in inhibitory cord blood samples that were held for 72 hours before testing (66.7%) compared with RPMI-1640 (45.0%). Less than 33% of all spiked plasma samples were detected by the BacT/ALERT 3D system. CONCLUSION: Diluting cord blood samples in culture medium containing bacterial growth promoting substances is a suitable option for sterility testing, whereas the use of plasma should be proscribed, because it might lead to false-negative results. Because inhibitory substances affect bacterial growth, inoculation of culture bottles should be done rapidly after sample preparation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".