A new multiplexed magnetic capture—Droplet digital PCR tool for monitoring wildlife population health and pathogen surveillance
Bibliographic record
Abstract
Abstract Anthropogenic stressors are exacerbating the emergence and spread of pathogens worldwide. In regions like the Arctic, where ecosystems are particularly susceptible, marked changes are predicted in regional diversity, intensity, and patterns of infectious diseases. To understand such rapidly changing host‐pathogen dynamics and mitigate the impacts of novel pathogens, we need sensitive disease surveillance tools. We developed and validated a novel multiplexed, magnetic capture, and ddPCR tool for the surveillance of multiple pathogens in polar bears, a sentinel species that is considered susceptible to climate change and other stressors with a pan‐Arctic distribution. Through sequence‐specific magnetic capture, we concentrated five target template sequences from three zoonotic bacteria ( Erysipelothrix rhusiopathiae , Francisella tularensis , and Mycobacterium tuberculosis complex) and two parasitic ( Toxoplasma gondii and Trichinella spp.) pathogens from large quantities (<100 g) of host tissue. We then designed and validated two multiplexed probe‐based ddPCR assays for the amplification and detection of the low‐concentration target DNA. Validations used 48 polar bear tissues (muscle and liver). We detected 14, 1, 3, 4, and 22 tissue positives for E. rhusiopathiae , F. tularensis , M. tuberculosis complex, T. gondii , and Trichinella spp., respectively. These multiplexed assays offer a rapid, specific tool for quantifying and monitoring the changing geographical and host distributions of pathogens relevant to human and animal health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".