Field Validation of SYBR Green- and TaqMan-Based Real-Time PCR Using Biopsy and Swab Samples To Diagnose American Tegumentary Leishmaniasis in an Area Where Leishmania (Viannia) braziliensis Is Endemic
Bibliographic record
Abstract
The precise diagnosis of American tegumentary leishmaniasis (ATL) is an essential task due to the disease's associated morbidity. A noninvasive, extremely sensitive, and highly specific exam is critical, particularly for mucosal leishmaniasis (ML), in which a low parasite quantity is expected. We aimed to compare the diagnostic accuracy of swab and biopsy sample analysis using SYBR Green- and TaqMan-based real-time PCR (qPCR) assays with that of a composite reference standard consisting of the Montenegro skin test, serology, histopathology, smears, culture, and conventional PCR. In total, 55 patients with ATL (ML, 18 patients; cutaneous leishmaniasis [CL], 37 patients) and 36 patients without ATL were studied. qPCR analysis of swabs was more accurate when using SYBR Green (87.88%; 95% confidence interval [CI], 77.86 to 93.73 patients) than when using TaqMan (78.79%; 95% CI, 67.49 to 86.92%) (P = 0.031). SYBR Green (84.72%; 95% CI, 74.68 to 91.25%) was also more accurate than TaqMan (73.61%; 95% CI, 62.42 to 82.41%) for biopsy samples (P = 0.008). All qPCR methods were 100% specific. Swabs and biopsy specimens had similar sensitivity when using the same chemistry (P = 0.125 for SYBR Green and P = 0.625 for TaqMan). Moreover, qPCR achieved better performance than most existing techniques used for the diagnosis of ATL and also detected the Leishmania parasite in a greater proportion of patients than the associated histopathology, smear, culture, and conventional PCR techniques did. Swabs therefore represent a useful diagnostic tool because they not only are noninvasive but also can achieve an accuracy similar to that of biopsy samples. The high accuracy of SYBR Green-based qPCR may also reduce the requirement for associated parasitological tests for ATL diagnosis.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".