Development and validation of a real-time PCR assay for the detection of clinical acanthamoebae
Bibliographic record
Abstract
BACKGROUND: Suboptimal agreement between molecular assays for the detection of Acanthamoeba spp. in clinical specimens has been demonstrated, and poor assay sensitivity directly imperils the vision of those affected by amoebic keratitis (AK) through delayed diagnosis. We sought to develop and validate a single Taqman real time PCR assay targeting the Acanthamoeba 18S rRNA gene that could be used to enhance sensitivity and specificity when paired with reference assays. METHODS: Biobanked DNA from surplus delinked AK clinical specimens and 10 ATCC strains of Acanthamoeba was extracted. Sequence alignment of 66 18S rRNA regions from 12 species of Acanthamoeba known to cause keratitis informed design of a new TaqMan primer set. Performance of the new assay was compared to the 2 assays used currently in our laboratory. RESULTS: Among 24 Acanthamoeba-positive and 83 negative specimens by the CDC reference standard, performance characteristics of the newly designed primer set were as follows: sensitivity 100%, specificity 94%, PPV 82.8%, and NPV 100%. Compared to culture, sensitivity of the new primer set was 100%, and specificity 96%. No cross-reactivity of the primer set to non-acanthamoebae, including Balamuthia and Naegleria, was found. CONCLUSIONS: We have validated a real time PCR assay for the diagnosis of AK, and in doing so, have overcome important barriers to rapid and sensitive detection of acanthamoebae, including limited sensitivity and specificity of commonly used assays.
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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 | 0.013 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".