Assessing the performance of TRX and DUF148 antigens for detection of prepatent Guinea worm (Dracunculus medinensis) infection in dogs
Bibliographic record
Abstract
Introduction Guinea worm (GW) is a nematode that causes a neglected tropical disease that is targeted for eradication. GW emergence in animals, particularly dogs, has hampered eradication efforts. Currently, there is no method for diagnosing GW infection in animals during the prepatent period. Previous work has identified two immunoreactive antigens, TRXL-1 (TRX) and DUF148. Methods This study developed and assessed the performance of an indirect ELISA using these antigens. Results Using serum samples from experimentally exposed dogs, TRX and DUF148 showed reactivity at 9- and 11-weeks post-exposure, respectively. These antigens were further assessed using sera of dogs from GW-endemic villages in Chad (n=47) and shelter dogs from the non-endemic United States (n=492). DUF148 showed better reactivity and sensitivity of 76.6.% in detecting GW infection in prepatent sera compared to TRX. However, DUF148 cross-reacted with a Brugia pahangi experimental infection serum sample and several shelter dog sera. To mitigate this cross-reaction, we produced 3 peptides that spanned different regions of DUF148. Peptide 3 from the C-terminal was more reactive with prepatent sera and had a sensitivity of 83%; however, the specificity was not superior to whole antigen. Discussion Our findings could facilitate the development of diagnostic methods for early detection of GW infection in dogs in endemic countries.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".