Detection and Species Identification of <i>Leishmania</i> DNA from Filter Paper Lesion Impressions for Patients with American Cutaneous Leishmaniasis
Bibliographic record
Abstract
BACKGROUND: Traditional detection of Leishmania from ulcers involves collection of invasive specimens that cause discomfort, require technical expertise, and carry risks of invasive procedures. We compared traditional diagnostic methods with a molecular noninvasive filter paper-based method for the diagnosis of cutaneous leishmaniasis. METHODS: Consecutive patients presenting to the Leishmania Clinic at Hospital Nacional Cayetano Heredia were enrolled. Polymerase chain reaction (PCR) was performed on lesion scrapings, aspirates, and filter paper impressions. The reference standard was any 2 of 5 tests positive: smear, aspirate culture, invasive-specimen PCR (scrapings and aspirates), filter paper PCR, and leishmanin skin test. Outcome measures were sensitivity and specificity. Leishmania speciation was performed by PCR-restriction fragment length polymorphism (RFLP) of positive specimens. RESULTS: Forty-five patients with 66 lesions were enrolled. Of 52 lesions diagnosed as cutaneous leishmaniasis, 50 were positive by PCR of invasive specimens versus 48 by PCR of filter papers (P=.930). Sensitivity and specificity of PCR on invasively obtained specimens were 94.2% (95% confidence interval [CI], 87.9%-100%) and 92.9% (95% CI, 79.4%-100%). Sensitivity and specificity of filter paper PCR were 92.3% (95% CI, 85.1%-99.5%) and 100%. Culture, smear, and leishmanin skin test all had inferior sensitivities, compared with PCR of invasive or noninvasive specimens (P<.001). Of 50 specimens positive by PCR, 19 had sufficient DNA for PCR-RFLP analysis. CONCLUSIONS: Filter paper PCR constitutes a sensitive and specific alternative to traditional diagnostic assays. This novel, rapid, well-tolerated method has the potential for widespread use in the field and in pediatric populations where traditional specimen collection is most difficult to perform, and can potentially be used for rapid species identification.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".