Seroprevalance of Leishmaniasis in Dogs from Hatay and Burdur Provinces of Turkey and Northern Cyprus
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the seroprevalance of leishmaniasis in dogs from Hatay and Burdur provinces of Turkey and Northern Cyprus. METHODS: Blood was collected from a total of 278 dogs, including 124 from Hatay, 49 from Burdur, and 105 from Northern Cyprus. Dilutions of serum samples were prepared, and the presence of anti-Leishmania antibodies was investigated by indirect fluorescent antibody technique (IFAT). RESULTS: A total of three dogs were found to be seropositive (1.1%), one from Hatay (0.8%) and two from Northern Cyprus (1.9%). Also, one dog (0.4%) from Northern Cyprus was found to be borderline positive. All dogs from Burdur have been identified as seronegative. CONCLUSION: This is the first research on the seroprevalence of the parasite in dogs from Hatay and Burdur. The seropositivity detected in dogs from Hatay and Northern Cyprus demonstrates the presence of the parasite in these regions, and obtained results contribute data on the prevalence of the disease in an epidemiological manner. To obtain more reliable data, it will be useful to conduct studies on wider dog populations and vector sandflies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".