Real-time PCR using FRET technology for Old World cutaneous leishmaniasis species differentiation
Bibliographic record
Abstract
BACKGROUND: Recently, there has been a re-emergence of cutaneous leishmaniasis in endemic countries and an increase in imported cases in non-endemic countries by travelers, workers, expatriates, immigrants, and military force personnel. Old World cutaneous leishmaniasis is caused primarily by Leishmania major, L. tropica and L. aethiopica. Despite their low sensitivity, diagnosis traditionally includes microscopic and histopathological examinations, and in vitro cultivation. Several conventional PCR techniques have been developed for species identification, which are time-consuming and labour-intensive. Real-time PCR using SYBR green dye, although provides rapid detection, may generate false positive signals. Therefore, a rapid and easy method such as a FRET-based real-time PCR would improve not only the turn-around time of diagnosing Old World cutaneous Leishmania species but will also increase its specificity and sensitivity. METHODS: A FRET-based real-time PCR assay which amplifies the cathepsin L-like cysteine protease B gene encoding a major Leishmania antigen was developed to differentiate L. major, L. tropica, and L. aethiopica in one single step using one set of primers and probes. Assay performance was tested on cutaneous and visceral strains of Leishmania parasite cultures and isolates of other protozoan parasites as well as human biopsy specimen. RESULTS: The assay readily differentiates between the three Old World cutaneous leishmaniasis species based on their melting curve characteristics. A single Tm at 55.2 ± 0.5 °C for L. aethiopica strains was distinguished from a single Tm at 57.4 ± 0.2 °C for L. major strains. A double curve with melting peaks at 66.6 ± 0.1 °C and 48.1 ± 0.5 °C or 55.8 ± 0.6 °C was observed for all L. tropica strains. The assay was further tested on biopsy specimens, which showed 100% agreement with results obtained from isoenzyme electrophoresis and Sanger sequencing. CONCLUSION: Currently, there are no published data on real-time PCR using FRET technology to differentiate between Old World cutaneous Leishmania species. In summary, our assay based on specific hybridization addresses the limitations of previous PCR technology and provides a single step, reliable method of species identification and rapid diagnostic applications.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".