Detection of serum antibodies against Leptospira spp. in brown rats (Rattus norvegicus) from Grenada, West Indies
Bibliographic record
Abstract
Background and Aim: Leptospirosis is an emerging disease of animals and humans. Among rodents brown rats (Rattus norvegicus) are an important reservoir of bacteria Leptospira. There is a paucity of information on reservoirs of Leptospira in Grenada. This study was conducted to estimate the prevalence of antibodies against Leptospira spp. in brown rats in a densely human populated area of Grenada. Materials and Methods: Blood samples from 169 brown rats were collected and sera screened for antibodies against Leptospira spp. using enzyme-linked immunosorbent assay. Results: Among a total of 169 brown rats trapped in two parishes in Grenada, 77/169 (45.5%) were positive for Leptospira spp. antibodies. A significant difference in seropositive population of brown rats between two collection sites was observed. No differences were found between sex and age of seropositive rats. Conclusion: Due to the close contact of brown rats with humans in Grenada, rats should be considered a high-risk factor in transmission of Leptospira to humans. Appropriate preventive measures should be instituted to prevent the transmission of Leptospira infection to humans.
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) | 0.000 | 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".