A FRET-Based Real-Time PCR Assay to Identify the Main Causal Agents of New World Tegumentary Leishmaniasis
Bibliographic record
Abstract
In South America, various species of Leishmania are endemic and cause New World tegumentary leishmaniasis (NWTL). The correct identification of these species is critical for adequate clinical management and surveillance activities. We developed a real-time polymerase chain reaction (PCR) assay and evaluated its diagnostic performance using 64 archived parasite isolates and 192 prospectively identified samples collected from individuals with suspected leishmaniasis enrolled at two reference clinics in Lima, Peru. The real-time PCR assay was able to detect a single parasite and provided unambiguous melting peaks for five Leishmania species of the Viannia subgenus that are highly prevalent in South America: L. (V.) braziliensis, L. (V.) panamensis, L. (V.) guyanensis, L. (V.) peruviana and L. (V.) lainsoni. Using kinetoplastid DNA-based PCR as a gold standard, the real-time PCR had sensitivity and specificity values of 92% and 77%, respectively, which were significantly higher than those of conventional tests such as microscopy, culture and the leishmanin skin test (LST). In addition, the real-time PCR identified 147 different clinical samples at the species level, providing an overall agreement of 100% when compared to multilocus sequence typing (MLST) data performed on a subset of these samples. Furthermore, the real-time PCR was three times faster and five times less expensive when compared to PCR - MLST for species identification from clinical specimens. In summary, this new assay represents a cost-effective and reliable alternative for the identification of the main species causing NWTL in South America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".