Mobile suitcase laboratory for rapid detection of Leishmania donovani using recombinase polymerase amplification assay
Bibliographic record
Abstract
BACKGROUND: Leishmania donovani (LD) is a protozoan parasite transmitted to humans from sand flies, which causes Visceral Leishmaniasis (VL). Currently, the diagnosis is based on presence of the anti-LD antibodies and clinical symptoms. Molecular diagnosis would require real-time PCR, which is not easy to implement at field settings. In this study, we report on the development and testing of a recombinase polymerase amplification (RPA) assay for the detection of LD. METHODS: A genomic DNA sample was applied to determine the assay analytical sensitivity. The cross-reactivity of the assay was tested by DNA of Leishmania spp. and of pathogens considered for differential diagnosis. The clinical performance of the assay was evaluated on LD positive and negative samples. All results were compared with real-time PCR. To allow the use of the assay at field settings, a mobile suitcase laboratory (56 × 45.5 × 26.5 cm) was developed and operated at the local hospital in Mymensingh, Bangladesh. RESULTS: The LD RPA assay detected equivalent to one LD genomic DNA. The assay was performed at constant temperature (42 °C) in 15 min. The RPA assay also detected other Leishmania species (L. major, L. aethiopica and L. infantum), but did not identify nucleic acid of other pathogens. Forty-eight samples from VL, asymptomatic and post-kala-azar dermal leishmaniasis subjects were detected positive and 48 LD-negative samples were negative by both LD RPA and real-time PCR assays, which indicates 100 % agreement. The suitcase laboratory was successfully operated at the local hospital by using a solar-powered battery. DNA extraction was performed by a novel magnetic bead based method (SpeedXtract), in which a simple fast lysis protocol was applied. Moreover, All reagents were cold-chain independent. CONCLUSIONS: The mobile suitcase laboratory using RPA is ideal for rapid sensitive and specific detection of LD especially at low resource settings and could contribute to VL control and elimination programmes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
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".