Epidemiological survey, molecular profiling and phylogenetic analysis of cutaneous leishmaniasis in Khyber Pakhtunkhwa, Pakistan
Bibliographic record
Abstract
BACKGROUND: Cutaneous leishmaniasis (CL), an emerging vector-borne ailment in Khyber Pakhtunkhwa (KPK), Pakistan, exhibits diverse spread patterns and outbreaks. METHODS: To comprehend its epidemiology and identify parasite species, we conducted an active survey on suspected CL cases (n=8845) in KPK. RESULTS: Microscopy and internal transcribed spacer-1 PCR-restriction fragment length polymorphism (RFLP) molecular techniques detected Leishmania spp. in blood samples. Phylogenetic analysis gauged genetic affinities with other areas. District Bannu displayed the highest CL impact (14.58%), while Swat had the lowest impact (4.33%) among cases. Annual blood examination rate, parasite incidence and slide positivity rate were 4.96 per 1000 people, 0.0233 and 0.047%, respectively. CL infections were prevalent in 1- to 20-y-olds, with males (57.17%) more vulnerable than females (42.82%). Single lesions occurred in 43.73% of patients, while 31.2% people had two lesions, 17.31% had three lesions and 7.74% had more than three lesions. Most had sand-fly exposure but lacked preventive measures like repellents and bed nets. Leishmania tropica was confirmed via RFLP analysis in amplified samples. Phylogenetic analysis unveiled genetic parallels between L. tropica of KPK and isolates from China, Iran, Afghanistan, India, Syria and Morocco. CONCLUSIONS: Urgent comprehensive control measures are imperative. Early detection, targeted interventions and raising awareness of CL and sand-fly vectors are vital for reducing the disease's impact. International collaboration and monitoring are crucial to tackle Leishmania spp.'s genetic diversity and curtail its cross-border spread.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".