Measurement of Immunoglobulin Concentrations in the Feces of Healthy Dogs
Bibliographic record
Abstract
Selective immunoglobulin A (IgA) deficiency is the most common primary immunodeficiency in humans and may be associated with chronic gastrointestinal disease. This observation has led to the suggestion that the high susceptibility of German shepherd dogs (GSD) to chronic enteropathies is related to a deficiency in mucosal IgA production. Relative deficiencies of IgA has been reported in the serum, saliva, tears, and feces of GSD both with and without alimentary disease; however, the findings of different studies are not consistent. The aim of this study was to confirm whether a relative deficiency of IgA exists in the feces of GSD. Feces were collected from healthy GSD (n = 209), Labrador retrievers (n = 96), beagles (n = 19), and miniature schnauzers (n = 32). Fecal IgA, IgM, and IgG were measured by capture enzyme-linked immunosorbent assays. Fecal IgG concentrations in the four breed groups were not significantly different. IgA concentrations were significantly greater in miniature schnauzers than in GSD (P = 0.0003) and Labradors (P = 0.0004) but not significantly different from those in beagles. IgM concentrations were significantly greater in miniature schnauzers than in GSD (P < 0.0001), Labradors (P < 0.0001), and beagles (P = 0.0098). These findings do not support the hypothesis that GSD have a relative deficiency in fecal IgA. The differences in immunoglobulin concentrations measured from a single defecation, between individuals of the same breed and between breeds, as well as the lack of an internal control molecule, make the determination of a normal reference range for all dogs impossible. Therefore, the usefulness of fecal immunoglobulin quantification for the assessment of intestinal immunoglobulin secretion in dogs is limited.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".