Use of PCR, IFAT and<i>in vitro</i>culture in the detection of<i>Leishmania infantum</i>infection in dogs and evaluation of the prevalence of canine leishmaniasis in a low endemic area in Tunisia
Bibliographic record
Abstract
The aim of this study was to assess the use of parasitological, serological and molecular methods for the detection of Leishmania infection in blood of 67 dogs and to investigate the prevalence of canine leishmaniasis (CanL) in Kairouan (central Tunisia), an area known to be of reduced endemicity and has not been studied since 1973. Veterinarians clinically examined all dogs, and the titer of anti-Leishmania antibodies was determined by indirect immune-fluorescence antibody test. The presence of Leishmania was performed by PCR and in vitro culture. IFAT was positive in 12% of dogs and promastigote form of the parasite was isolated by in vitro culture from only 4.5% of them. However, DNA of Leishmania was detected by PCR in 20.9% of dogs. PCR was more sensitive than IFAT (p = 0.004) and in vitro culture (p < 10(-5)). A prevalence of 21% was found in Kairouan, which is significant high (p < 10(-3)) when compared to that of thirty years ago. This state is in correlation with the increase in other Mediterranean countries. Furthermore, 50% of positive dogs were asymptomatic. Preventive measures must be taken against these dogs as for symptomatic ones since their role in the transmission of the infection to vectors has been proven.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".