Seropositivity of Leptospira in rodents, shrews, and domestic animals in Unguja, Tanzania
Bibliographic record
Abstract
Background: Leptospirosis is one of the most commonly neglected zoonoses in developing nations including Tanzania. This study aims to find out the seroprevalence of leptospirosis in rodents, shrews, and domestic animals in different regions in Unguja Island, Tanzania. Methods: A cross-sectional study was carried out from January to April 2022. The blood samples were collected from rodents and shrews (n=248), cattle (n=247), goats (n=130), sheep (n=32), and dogs (n=80). The blood samples were allowed to clot in a slanted position and serum samples were harvested. A microscopic agglutination test (MAT) was performed on the sera to check for leptospiral antibodies using five Leptospira serovars as antigens (Sokoine, Lora, Pomona, Grippotyphosa and Hebdomadis). Results: The overall seropositivity of leptospiral antibodies was 9.68% in rodents and shrews, 14.57% in cattle, 10.01% in goats, 31.25% in sheep, and 26.25% in dogs. The seropositivity of Leptospira varied significantly with animal species (OR=1.9, 95 % CI:1.1-3.3, p=0.03). The most frequently detected serovar was Sokoine (27.89%), followed by Pomona (19.47%), Lora (18.26%), Grippotyphosa (17.98%), and Hebdomadis (8.16%), respectively. Conclusion: Our study suggests that further research should be conducted to find out factors of high seropositivity of leptospiral in Unguja. Vaccination of domestic animals with vaccines against local Leptospira strains should be encouraged, and rodent control and public awareness should be emphasized.
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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 | 0.000 |
| 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".