High prevalence of vector-borne protozoa and bacteria in dogs from Chad determined using a targeted next-generation sequencing approach
Bibliographic record
Abstract
Data on vector-borne pathogens infecting dogs from sub-Saharan Africa is limited. In this study, we assessed the prevalence of VBPs, their associated risk factors, and pathogen interactions in domestic dogs. Whole blood samples were obtained for 1202 apparently healthy dogs in Chad from September to October 2021, and nucleic acids were extracted and then subjected to a targeted next-generation sequencing (tNGS) assay for detection of 15 VBPs. Overall, 88.7% of the dogs were positive for at least one pathogen, and 62.9% were coinfected with two or more VBPs. The most frequent pathogen detected was Hepatozoon canis in 62.4% of the dogs, Mycoplasma haemocanis in 59.2%, Anaplasma platys in 29.2%, Candidatus Mycoplasma haematoparvum in 21.2%, Ehrlichia canis in 20.3%, Babesia vogeli in 2.0% and Candidatus Mycoplasma turicensis in 1.5%. While most of the dogs (62.9%) were co-infected with two or more VBPs, having an infection with three pathogens (30.8%) was more common. According to multivariable logistic regression analysis, being a senior dog and residing in Chari Baguirmi south were identified as potential risk factors for infection by most of the pathogens. Network analyses revealed complex interactions suggesting facilitative associations among VBPs. These results are useful in expanding the knowledge of VBPs in Africa and establishing a baseline for downstream studies into hemotropic mycoplasmas.
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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 | 0.001 |
| 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".