Accurate Diagnosis of Tick-borne Diseases in Working Dogs: The Impact of Unseen Risk Factors
Bibliographic record
Abstract
Aims: Canine vector-borne diseases (CVBDs) are a significant concern in India due to their widespread prevalence and impact on working dogs. This study aimed to screen National Disaster Response Force (NDRF) dogs in Arakkonam for canine hemoprotozoans using microscopy and PCR, while also assessing haematological and serum biochemical parameters as a part of regular health check-up. Methodology: The study was conducted in April 2024 at the 4th Battalion of the National Disaster Response Force (NDRF) in Arakkonam, Tamil Nadu, India. Blood samples from 39 dogs were examined using microscopy, complete blood count (CBC), serum biochemistry analysis, and PCR, including both hemoprotozoan and nested hemoprotozoan panels. The data related to hematological and serum biochemical parameters, as well as the molecular prevalence of hemoprotozoans, were statistically analyzed using Mean ± SD and Fisher's exact test in SPSS software. Results: While microscopy did not detect piroplasms, PCR revealed Babesia spp. (28.2%), Ehrlichia canis (2.56%), and Anaplasma platys (23.1%). Nested PCR further identified Babesia gibsoni (56.4%) being the most prevalent, followed by Babesia vogeli (10.3%). Coinfections were observed in 23% (9/39) of dogs. Older dogs (>1 year) had a significantly higher infection rate than younger dogs. Labrador Retrievers showed higher infection rates, suggesting a possible breed-specific susceptibility. Conclusion Despite tick control efforts, NDRF dogs remain at high risk due to environmental factors and interactions with stray dogs. Subclinical infections highlight the need for regular screenings and preventive measures. The findings emphasize the need for comprehensive disease management strategies, including treatment of infected dogs, environmental tick control, and adherence to preventive protocols to potentially reduce transmission risks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".