Rapid, Sensitive, and Discriminating Identification of<i>Naegleria</i>spp. by Real-Time PCR and Melting-Curve Analysis
Bibliographic record
Abstract
The free-living amoeboflagellate genus Naegleria includes one pathogenic and two potentially pathogenic species (Naegleria fowleri, Naegleria italica, and Naegleria australiensis) plus numerous benign organisms. Monitoring of bathing water, water supplies, and cooling systems for these pathogens requires a timely and reliable method for identification, but current DNA sequence-based methods identify only N. fowleri or require full sequencing to identify other species in the genus. A novel closed-tube method for distinguishing thermophilic Naegleria species is presented, using a single primer set and the DNA intercalating dye SYTO9 for real-time PCR and melting-curve analysis of the 5.8S ribosomal DNA gene and flanking noncoding spacers (ITS1, ITS2). Collection of DNA melting data at close temperature intervals produces highly informative melting curves with one or more recognizable melting peaks, readily distinguished for seven Naegleria species and the related Willaertia magna. Advantages over other methods used to identify these organisms include its comprehensiveness (encompassing all species tested to date), simplicity (no electrophoresis required to verify the product), and sensitivity (unambiguous identification from DNA equivalent to one cell). This approach should be applicable to a wide range of microorganisms of medical importance.
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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 | 0.002 |
| 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) | 0.001 | 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".