Detecting total immunoglobulins in diverse animal species with a novel split enzymatic assay
Bibliographic record
Abstract
BACKGROUND: Total immunolobulin G concentration is a useful, albeit underutilized, diagnostic parameter for health assessments of non-domestic animal species, due to a lack of functional diagnostic tools. Traditional assays, including enzyme-linked immunosorbent assay or radial immunodiffusion, require development of specific reagents (e.g., polyclonal antisera and appropriate protocols) for each animal species, precluding wide and easy adoption in wildlife welfare. As an alternative, bacterial virulence factors able to bind IgGs in antigen-independent manner can be used. To further simplify the diagnostic procedure and increase the number of species recognized by an assay, in this study a recently developed Split Trehalase immunoglobulin assay (STIGA) with bIBPs as a sensing elements was used to detect antibodies in 29 species from 9 orders. Three bacterial immunoglobulin binding proteins (protein G, protein A and protein L) were incorporated into STIGA reagents to increase the number of species recognized. RESULTS: IgG concentrations were detected through glucose production and produced signals were categorized in 4 categories, from not active to strong signal. Activation was detected in almost all tested animal species, apart from birds. Incorporation of Protein G, Protein A and Protein L allowed detection of IgGs in 62, 15.5 and 6.9% of species with a strong signal, respectively. Assays combining 2 bacterial immunoglobulin binding proteins as sensing element generally gave poorer performance than assays with the same bacterial immunoglobulin binding proteins fused to both trehalase fragments. CONCLUSIONS: STIGA assays have potential to be further developed into an easily adoptable diagnostic test for total amount of IgGs in almost any serum sample, independent of species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".