Microfluidic PCR and network analysis reveals complex tick-borne pathogen interactions in the tropics
Bibliographic record
Abstract
Abstract Background Ixodid ticks, particularly Rhipicephalus sanguineus s.l., are important vectors of various disease-causing agents in dogs and humans in Cuba. However, our understading of interactions among tick-borne pathogens (TBPs) in infected dogs or the vector R. sanguineus s.l. remains limited. This study integrates microfluidic-based high-throughput real-time PCR data, Yule's Q statistic, and network analysis to elucidate pathogen-pathogen interactions in dogs and ticks in tropical western Cuba. Methods A cross-sectional study involving 46 client-owned dogs was conducted. Blood samples were collected from these dogs, and ticks infesting the same dogs were morphologically and molecularly identified. Nucleic acids were extracted from both canine blood and tick samples. Microfluidic-based high-throughput real-time PCR was employed to detect 25 bacterial species, 10 parasite species, 6 bacterial genera, and 4 parasite taxa, as well as to confirm the identity of the collected ticks. Validation was performed through end-point PCR assays and DNA sequencing analysis. Yule's Q statistic and network analysis were used to analyse the associations between different TBP species based on binary presence-absence data. Results The study revealed a high prevalence of TBPs in both dogs and R. sanguineus s.l., the only tick species found on the dogs. Hepatozoon canis and Ehrlichia canis were among the most common pathogens detected. Co-infections were observed, notably between E. canis and H. canis. Significant correlations were found between the presence of Anaplasma platys and H. canis in both dogs and ticks. A complex co-occurrence network among haemoparasite species was identified, highlighting potential facilitative and inhibitory roles. Notably, H. canis was found as a highly interconnected node, exhibiting significant positive associations with various taxa, including A. platys, and E. canis, suggesting facilitative interactions among these pathogens. Phylogenetic analysis showed genetic diversity in the detected TBPs. Conclusions Overall, this research enhances our understanding of TBPs in Cuba, providing insights into their prevalence, associations, and genetic diversity, with implications for disease surveillance and management. Graphical abstract
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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 | 0.001 | 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".