Development and evaluation of a modified agglutination test for diagnosis of Toxoplasma infection using tachyzoites cultivated in cell culture
Bibliographic record
Abstract
Toxoplasma gondii is a zoonotic protozoan parasite that can cause significant disease in most warm-blooded animals, including humans. Surveillance testing of human and animal populations is essential to estimate disease prevalence, assess food safety risks and establish control programmes. Serological tests are the most practical methods to detect the prevalence of infection in a broad range of host populations. The modified agglutination test (MAT) is a sensitive serological method to detect Toxoplasma IgG antibodies in livestock and wild animals. An in-house MAT was developed using tachyzoites produced by in vitro cultivation instead of traditional propagation in murine peritoneal cavity. The assay was evaluated using samples of serum and/or meat juice from pigs and cats experimentally infected with T. gondii . Samples were also tested by a commercially available MAT kit. Comparative analysis of test results from serum and meat juice samples showed excellent agreement between the in-house MAT and the commercial MAT. Serum and/or blood samples from naturally infected cats, sheep, and 20 wildlife host species were also tested by the in-house MAT, with overall results comparable to those obtained using the commercial MAT kit. Therefore, this new MAT is an efficient and convenient method for testing a variety of terrestrial and aquatic domestic or wild host species for T. gondii .
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".