Development of an ethidium monoazide–enhanced internally controlled universal 16S rDNA real‐time polymerase chain reaction assay for detection of bacterial contamination in platelet concentrates
Bibliographic record
Abstract
BACKGROUND: Bacterial contamination of platelet (PLT) concentrates remains a problem for blood transfusion services. Culture-based bacterial screening techniques are available but offer inadequate speed and sensitivity. Alternative techniques based on polymerase chain reaction (PCR) amplification have been described but their performance is often compromised by traces of bacterial DNA in reagents. STUDY DESIGN AND METHODS: Universal 16S rDNA primers were used to develop a real-time PCR assay (TaqMan, Applied Biosystems) and various reagent decontamination strategies were explored. Detection sensitivity was assessed by spiking PLT concentrates with known concentrations of 13 different organisms. RESULTS: Restriction enzyme digestion, master mix ultrafiltration, and use of alternative Taq polymerases all reduced the level of reagent DNA contamination to some extent but all proved unreliable. In contrast, ethidium monoazide (EMA) treatment of the PCR master mix followed by photoactivation was reliable and effective, permitting a full 40 amplification cycles, and totally eliminated contamination without compromising assay sensitivity. All 13 organisms were efficiently detected and the limit of detection for Escherichia coli-spiked PLTs was approximately 1 colony-forming unit/mL. Coamplification of human mitochondrial DNA served to confirm efficient nucleic acid extraction and the absence of PCR inhibition in each sample. One of five automated extraction platforms evaluated was found to be contamination free and capable of high-throughput processing. CONCLUSION: Cross-linking of EMA to DNA via photoactivation solved the previously intractable problem of reagent contamination and permitted the development of a high-sensitivity universal bacterial detection system. Trials are ongoing to assess the suitability of the system for high-throughput screening of PLT concentrates.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".