Gauging the skin resident Leishmania parasites through a loop mediated isothermal amplification (LAMP) assay in post-kala-azar dermal leishmaniasis
Bibliographic record
Abstract
Despite the availability of highly sensitive polymerase chain reaction (PCR)-based methods, the dearth of remotely deployable diagnostic tools circumvents the early and accurate detection of individuals with post-kala-azar dermal leishmaniasis (PKDL). Here, we evaluate a design-locked loop-mediated isothermal amplification (LAMP) assay to diagnose PKDL. A total of 76 snip-skin samples collected from individuals with probable PKDL (clinical presentation and a positive rK39 rapid diagnostic test (RDT)) were assessed by microscopy, qPCR, and LAMP. An equal number of age and sex-matched healthy controls were included to determine the specificity of the LAMP assay. The LAMP assay with a Qiagen DNA extraction (Q-LAMP) showed a promising sensitivity of 72.37% (95% CI: 60.91-82.01%) for identifying the PKDL cases. LAMP assay sensitivity declined when the DNA was extracted using a boil-spin method. Q-qPCR showed 68.42% (56.75-78.61%) sensitivity, comparable to LAMP and with an excellent agreement, whereas the microscopy exhibited a weak sensitivity of 39.47% (28.44-51.35%). When microscopy and/or qPCR were considered the gold standard, Q-LAMP exhibited an elevated sensitivity of 89.7% (95% CI: 78.83-96.11%) for detection of PKDL cases and Bayesian latent class modeling substantiated the excellent sensitivity of the assay. All healthy controls were found to be negative. Notwithstanding the optimum efficiency of the LAMP assay towards the detection of PKDL cases, further optimization of the boil-spin method is warranted to permit remote use of the assay.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".