Evaluation of Loopamp Leishmania detection kit for the diagnosis of cutaneous leishmaniasis in Ethiopia
Bibliographic record
Abstract
BACKGROUND: Cutaneous leishmaniasis (CL) in Ethiopia and some parts of Kenya is predominantly caused by Leishmania aethiopica. While skin-slit (SS) microscopy is routinely used for CL diagnosis, more sensitive molecular tests are available. The Loopamp™ Leishmania detection kit (Loopamp) is a robust loop-mediated isothermal amplification (LAMP) assay with the potential for implementation in primary healthcare facilities. In this study, we comparatively assessed the diagnostic accuracy of four methods currently used to diagnose CL: Loopamp, kinetoplast DNA (kDNA) PCR, spliced leader RNA (SL-RNA) PCR and SS microscopy. METHODS: A study on 122 stored tape disc samples of suspected CL patients was conducted in Gondar, northwestern Ethiopia. Routine SS microscopy results were obtained from all patients. Total nucleic acids were extracted from the tapes and subjected to PCR testing targeting kDNA and SL-RNA, and Loopamp. Diagnostic accuracy was calculated with SS microscopy as a reference test. The limit of detection (LoD) of Loopamp and kDNA PCR were determined for cultured L. aethiopica and Leishmania donovani. RESULTS: Of the 122 patients, 64 (52.5%) were identified as CL cases based on SS microscopy. Although the PCR tests showed a sensitivity of 95.3% (95% confidence interval [CI] 91.6-99.1), Loopamp only had 48.4% (95% CI 39.6-57.3) sensitivity and 87.9% (95% CI 82.1-93.7) specificity. The LoD of Loopamp for L. donovani was 100-fold lower (20 fg/µl) than that for L. aethiopica (2 pg/µl). CONCLUSIONS: The Loopamp™ Leishmania detection kit is not suitable for the diagnosis of CL in Ethiopia, presumably due to a primer mismatch with the L. aethiopica 18S rRNA target. Further research is needed to develop a simple and sensitive point-of-care test that allows the decentralization of CL diagnosis in Ethiopia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".