COMPARISON OF SYNTHETIC TYVELOSE ANTIGEN WITH EXCRETORY–SECRETORY ANTIGEN FOR THE DETECTION OF TRICHINELLOSIS IN SWINE USING ENZYME-LINKED IMMUNOSORBENT ASSAY
Bibliographic record
Abstract
Two enzyme-linked immunosorbent assay (ELISA) systems, one using natural excretory-secretory (ES) antigens and the other a synthetic glycan antigen (3,6-dideoxy-D-arabinohexose [tyvelose, TY]), were evaluated for the serological diagnosis of trichinellosis in swine. Sensitivity was estimated using samples (n = 113) collected 3-21 wk PI from 15 experimentally infected pigs, and specificity was estimated using samples (n = 397) from a population of Trichinella spp.-free pigs. Results were analyzed using 2 cutoff values recommended in international guidelines (Office Internationale des Epizooties [OIE]) and by the optimal cutoff level as determined by receiver-operator characteristic (ROC) analysis. The ROC-optimized TY-ELISA consistently performed better than all other combinations. None of the combinations of test and cut-off detected infected pigs sooner than 35 days; however, the ROC-optimized TY-ELISA identified 8 of 15 pigs earlier than the ES-ELISA and detected 2 pigs missed by all other tests. At 49 days PI the sensitivity and specificity of the ROC-optimized TY-ELISA were 94.3 and 96.7%, respectively, as compared with the ROC-optimized ES-ELISA at 84.9 and 96.0%, respectively. The ROC-optimized TY-ELISA was 100% specific at OIE-recommended cut-offs. This study indicates that the TY-ELISA is as good or better than the ES-ELISA for the detection of trichinellosis in swine.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".