Cutaneous leishmaniasis in the 21st century: from the laboratory to the bedside
Bibliographic record
Abstract
PURPOSE OF REVIEW: Despite modern advances in molecular diagnostic tools and a better understanding of its complex pathophysiology, cutaneous leishmaniasis, a neglected tropical disease, remains a major global health problem. Laboratory methods to inform prognosis and treatment are not widely available, the therapeutic options are limited and have significant adverse effects, and emergence of drug resistance is a further complication. New advances in the understanding of the role of Leishmania RNA virus (LRV) as a prognostic factor, speciation methods and antimicrobial resistance testing and their limitations will be discussed. RECENT FINDINGS: LRV, an intracytoplasmic endosymbiont found mostly in Leishmania spp. associated with more severe disease, appears to play a role in modulating the host immune response and has been associated with treatment failure in some Viannia subgenus species. Proper speciation is an important guide to management. However, recent findings have demonstrated significant heterogeneity of results related to differences in genotyping methods. SUMMARY: Recognition of the role of LRV in immune modulation and response to treatment along with more accessible tools for its detection to guide management at the bedside should allow a better individualized approach. Improving accessibility and standardization of speciation methods and antimicrobial susceptibility testing should be major goals to improve cutaneous leishmaniasis management in the 21st century.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".