Development of a Genetic Assay to Distinguish between Leishmania viannia Species on the Basis of Isoenzyme Differences
Bibliographic record
Abstract
BACKGROUND: Tegumentary leishmaniasis in Latin America is caused mainly by Leishmania viannia braziliensis complex parasites. L. braziliensis and Leishmania viannia peruviana are the 2 predominant Leishmania species in Peru. L. braziliensis is more virulent, because it can cause mucocutaneous leishmaniasis, known as espundia, that results in severe facial destruction. Early identification of the species that causes the initial cutaneous infection would greatly help to prevent mucocutaneous leishmaniasis, because it would allow more aggressive treatment and follow-up. However, because of the close genetic similarity of L. braziliensis and L. peruviana, there currently exists no simple assay to distinguish between these species. METHODS: We cloned the mannose phosphate isomerase gene from both L. braziliensis and L. peruviana. It is the only known isoenzyme capable of differentiating between L. braziliensis and L. peruviana in multilocus enzyme electrophoresis. Interestingly, only a single nucleotide polymorphism was found between the mannose phosphate isomerase genes from L. braziliensis and L. peruviana, resulting in an amino acid change from threonine to arginine at amino acid 361. A polymerase chain reaction assay was developed to distinguish the single nucleotide polymorphism of the mannose phosphate isomerase gene to allow for the specific identification of L. braziliensis or L. peruviana. RESULTS: This assay was validated with 31 reference strains that were previously typed by multilocus enzyme electrophoresis, successfully applied to patient biopsy samples, and adapted to a real-time polymerase chain reaction assay. CONCLUSIONS: This innovative approach combines new genetic knowledge with traditional biochemical fundamentals of multilocus enzyme electrophoresis to better manage leishmaniasis in Latin America.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".