Re-emergence of canine Leishmania infantum infection in mountain areas of Beijing
Bibliographic record
Abstract
Abstract Canine Leishmaniasis (CanL) is an endemic infectious disease in China, causing visceral Leishmaniasis (VL) and resulting in important public health problem. However, in the last 3 y, endemic trends have changed considerably and spatial–temporal aggregation areas have shifted from northwestern to central China. Although Beijing was an endemic area for CanL in the last century, this disease has not been reported in Beijing since control programs were implemented in the 1950s. In the present study, PCR and immunochromatographic (ICT) were used to estimate prevalence of Leishmania infection in domestic dogs living in Beijing, a VL re -emergencearea. In total, 4420 canine blood samples were collected at vet clinics in 14 districts of Beijing. Overall prevalence (percentage of dogs seropositive and/or PCR positive) of CanL infection in Beijing was 1.22% (54/4420). However, prevalence of CanL in the western mountain areas was 4.68% (45/961), significantly higher than that (0.26%, 9/3459) of the plains. In addition, multilocus sequence typing (MLST) of seven enzyme-coding genes was used to examine phylogenetic relationships of CanL strains. Forty-one Leishmania infantum isolates were well separated from the other strains and divided into five major clades (A to E) by MLST analysis. All clades were closely related to strains from Sichuan Province and Gansu Province. A phylogenetic tree, based on the MLST, revealed that L. infantum in Beijing was genetically related to strains from western endemic of Mountain type VL in China. In conclusion, CanL has re-emerged in Beijing, and almost 5% of dogs living in Beijing’s mountain areas were infected with L. infantum . The phylogenetic tree based on MLST effectively distinguished species of Leishmania and reflected geographical origins. Because dogs are considered a natural reservoir, comprehensive control measures including surveillance, phylogenetic analyses and management should be implemented to mitigate or eliminate Leishmaniasis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".