Identification of<i>Burkholderia cepacia</i>Complex by PCR: A Simple Way
Bibliographic record
Abstract
United States Pharmacopeia (USP) General Chapter <60> for the detection of Burkholderia cepacia complex (Bcc) members in nonsterile products became official in December 2019. This isolation method requires confirmation of the identity of any growth found on Burkholderia cepacia Selective Agar (BCSA) by additional identification tests (refer to the Interpretation section). This article presents a singleplex polymerase chain reaction (PCR) method to rapidly confirm the membership of any microbial grown on BCSA (and other nutrient medium) in the Bcc group. This method is cost effective as it does not require expensive equipment or reagents; therefore, it can be easily adopted in the industry without an important investment. We validated this singleplex PCR Bcc identification method with previously published PCR primers with an expanded panel of 37 clinical and environmental Bcc isolates. The sources and repositories of these Bcc isolates include contaminated health products and medical devices, patients infected with cystic fibrosis, the National Microbiology Laboratory (NML) internal strain bank, and the American Type Culture Collection (ATCC). All 37 isolates that belong to the Bcc tested positive using our confirmatory identification method. Twenty-two negative controls including four isolates belonging to the genus Burkholderia tested negative as expected. Our work indicates that this singleplex PCR is an efficient confirmatory method for Bcc identification, and it can successfully supplement USP <60> for Bcc isolates identification found in pharmaceutical products.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 0.004 |
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".