Quantitative Kinetoplast DNA Assessment During Treatment of Mucosal Leishmaniasis as a Potential Biomarker of Outcome: A Pilot Study
Bibliographic record
Abstract
Mucosal leishmaniasis (ML) is a disfiguring manifestation of Leishmania (Viannia) infection. We evaluated parasite load (PL) over time as a potential biomarker of treatment outcome in ML. PL was assessed with kinetoplast DNA quantitative real-time polymerase chain reaction (kDNA-qPCR) at enrollment, days 14 and 21-28 of therapy and 3, 6, 12-18, and 18-24 months after treatment of ML and correlated to demographic, clinical, and parasitologic factors. Forty-four patients were enrolled: 30 men and 14 women. Enrollment PL differed significantly by causative species (P < 0.001), and was higher in patients with severe ML (nasal and laryngeal involvement) compared with those with only isolated nasal involvement (median = 1,285 versus 51.5 parasites/μg tissue DNA; P = 0.005). Two patterns of PL emerged: pattern 1 (N = 23) was characterized by a sequential decline in PL during and after therapy until kDNA was undetectable. Pattern 2 (N = 18) was characterized by clearance of detectable kDNA during treatment, followed by an increased PL thereafter. All patients who failed treatment (N = 4) demonstrated pattern 1. Leishmania (Viannia) braziliensis was overrepresented among those with pattern 2 (P = 0.019). PL can be quantified by cytology brush qPCR during and after treatment in ML. We demonstrate that treatment failure was associated with undetectable PL, and L. (V.) braziliensis infection was overrepresented in those with rebounding PL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".