Immunochromatographic assay using gold nanoparticles for measuring salivary secretory IgA in dogs as a stress marker
Bibliographic record
Abstract
The concentration of salivary secretory immunoglobulin A (sIgA) is a well-known stress marker for humans. The concentration of salivary sIgA in dogs has also been reported as a useful stress marker. In addition, salivary sIgA in dogs has been used to determine the adaptive ability of dogs for further training. There are conventional procedures based on enzyme-linked immunosorbent assay (ELISA) for measuring salivary sIgA in dogs. However, ELISA requires long assay time, complicated operations and is costly. In the present study, we developed an immunochromatographic assay for measuring salivary sIgA in dogs using a dilution buffer containing a non-ionic surfactant. We determined 2500-fold dilution as the optimum condition for dog saliva using a phosphate buffer (50 mM, pH 7.2) containing non-ionic surfactant (3 wt% Tween 20). The results obtained from the saliva samples of three dogs using immunochromatographic assay were compared with those obtained from ELISA. It was found that the immunochromatographic assay is applicable to judge the change in salivary sIgA in each dog. The immunochromatographic assay for salivary sIgA in dogs is a promising tool, which should soon become commercially available for predicting a dog's psychological condition and estimating adaptive ability for training as guide or police dogs.
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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 | 0.000 | 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) | 0.001 | 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".