Development of a PCR Assay for the Detection of Legionella micdadei in the Environment
Bibliographic record
Abstract
Background/Objectives: Legionella micdadei is a clinically significant species within the Legionella genus, requiring accurate detection methods, surveillance, and precise clinical diagnosis. Our objective was to develop a sensitive polymerase chain reaction (PCR) assay specific for L. micdadei to detect its presence in environmental specimens. Methods: We targeted the 23S–5S intergenic spacer region, which can differentiate Legionella spp. We tested the detection of L. micdadei with 20 strains and determined the limit of detection with 2 strains. We verified assay specificity with 17 strains of other Legionella spp., 62 strains of other bacterial and fungal genera, and three human DNA specimens. We evaluated intra- and inter-run precision. We tested 15 environmental specimens (water, swabs of water faucets, mulch, and soil) by PCR. Results: The PCR assay demonstrated 100% analytical specificity (no cross-reactivity with non-targeted species), 100% inclusivity (detection of all L. micdadei strains), and high precision, with a coefficient of variation ≤ 2% across replicates. The limit of detection was estimated at 5 genomic DNA copies per reaction. We detected L. micdadei in environmental specimens. Conclusions: This PCR assay enables accurate detection of L. micdadei and is not subject to competition with other Legionella spp., thereby addressing limitations of current broad-spectrum Legionella approaches. The evaluation supports its application in environmental detection for surveillance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 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".