Non-Invasive Cytology Brush PCR for the Diagnosis and Causative Species Identification of American Cutaneous Leishmaniasis in Peru
Bibliographic record
Abstract
BACKGROUND: Traditional methods of detecting Leishmania from cutaneous lesions involve invasive diagnostic procedures, such as scrapings, which cause discomfort, require technical expertise, and carry risks of invasive procedures. We compared the performance of 2 novel, molecular-based non-invasive methods for the diagnosis of cutaneous leishmaniasis (CL). METHODS: Consecutive patients presenting to the Leishmania Clinic at the Hospital Nacional Cayetano Heredia were enrolled. PCR was performed on filter paper lesion impressions (FPLIs), cytology brushes, and lancets for detection of Leishmania DNA. Smears from lesion scrapings and leishmanin skin test were also performed. Outcome measures were sensitivity and specificity. Composite reference standard was any 2 of 5 tests positive. Species identification was performed by PCR assays of positive specimens. RESULTS: Ninety patients with 129 lesions were enrolled, 117 of which fulfilled reference criteria for a diagnosis of CL. Of these 117 lesions, 113 were positive by PCR of lancets used for lesion scrapings versus 116 by PCR of FPLIs (p=0.930) or 116 by PCR of cytology brushes (p=0.930). Sensitivity and specificity of PCR on lancets were 96.6% [95% CI 93.3-99.9%] and 100%, respectively. Sensitivity and specificity of FPLI PCR were 99.1% [95% CI 97.4-100%] and 100%, respectively. Sensitivity and specificity of cytology brush PCR were 99.1% [95% CI 97.4-100%] and 100%, respectively. Giemsa-stained lesion smear and leishmanin skin test had inferior sensitivities at 47.9% [95% CI 38.9-57.0%] and 82.3% [95% CI 73.9-90.7%], respectively, compared to PCR of invasive or non-invasive specimens (p<0.001). CONCLUSIONS: Cytology brush PCR constitutes a sensitive and specific alternative to traditional diagnostic assays performed on invasive specimens such as lesion scrapings. It performs comparatively to non-invasive FPLI PCR. This novel, rapid, and well-tolerated method has the potential for widespread use in the field and in pediatric populations where traditional specimen collection is difficult.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".