Evaluation of Real-time PCR for Diagnosis of Post-Kala-azar Dermal Leishmaniasis in Endemic Foci of Bangladesh
Bibliographic record
Abstract
Abstract Background Post-kala-azar dermal leishmaniasis (PKDL) is a sequel to visceral leishmaniasis (VL), which is found in VL-endemic countries including Bangladesh. Because of these enigmatic cases, the success of the National Kala-azar Elimination Program is under threat. To date, diagnostic methods for PKDL cases in endemic regions have been limited to clinical examination and rK39 test or microscopy, and a suitable and accurate alternative method is needed. In this study, we investigated the application of real-time polymerase chain reaction (PCR) as a potential method for diagnosis of PKDL in comparison with microscopy. Methods Ninety-one suspected macular PKDL cases from Mymensingh district, Bangladesh, were enrolled in the study after diagnosis by clinical examination and an rK39 strip test. All of them responded after completion of the treatment with miltefosine. During enrollment, a skin biopsy was done for each patient, and both microscopy and real-time PCR were performed for detection and quantification of Leishmania donovan body (LDB) and LD DNA, respectively. Results Real-time PCR detected 83 cases among all suspected PKDL patients, with an encouraging sensitivity of 91.2% (83.4%–96.1%), whereas microscopy showed 50.6% (39.9%–61.2%) sensitivity. Among all suspected PKDL cases, 42 cases were positive in both microscopy and qPCR, whereas 41 cases were detected as positive through qPCR only. Conclusions This study provides evidence that real-time PCR is a promising tool for diagnosis of PKDL in endemic regions. In addition to diagnosis, the quantitative ability of this method could be further exploited for after-treatment prognosis and cure assessment of PKDL cases.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".