The proof is in the poo: Non‐invasive method to detect endoparasitic infection
Bibliographic record
Abstract
Almost every animal trait is strongly associated with parasitic infection or the potential exposure to parasites. Despite this importance, one of the greatest challenges that researchers still face is to accurately determine the status and severity of the endoparasitic infection without killing and dissecting the host. Thus, the precise detection of infection with minimal handling of the individual will improve experimental designs in live animal research. Here, we quantified extracellular DNA from two species of endoparasitic worm that grow within the host body cavity, hairworms (phylum Nematomorpha) and mermithids (phylum Nematoda), from the frass of their insect host, a cave wētā (Orthoptera: Rhaphidophoridae) and an earwig (Dermaptera: Forficulidae), respectively. Frass collection was done at two successive time periods, to test if parasitic growth correlated with relative DNA quantity in the frass. We developed and optimized two highly specific TaqMan assays, one for each parasite-specific DNA amplification. We were able to detect infection prevalence with 100% accuracy in individuals identified as infected through post-study dissections. An additional infection in earwigs was detected with the TaqMan assay alone, probably because some worms were either too small or degraded to observe during dissection. No difference in DNA quantity was detected between sampling periods, although future protocols could be refined to support such a trend. This study demonstrates that a noninvasive and minimally stressful method can be used to detect endoparasitic infection with greater accuracy than dissection alone, helping improve protocols for live animal studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".