Anti‐<scp>GM2</scp> ganglioside antibodies are a biomarker for acute canine polyradiculoneuritis
Bibliographic record
Abstract
Acute canine polyradiculoneuritis (ACP) is considered to be the canine equivalent of the human peripheral nerve disorder Guillain-Barré syndrome (GBS); an aetiological relationship, however, remains to be demonstrated. In GBS, anti-glycolipid antibodies (Abs) are considered as important disease mediators. To address the possibility of common Ab biomarkers, the sera of 25 ACP dogs, 19 non-neurological, and 15 epileptic control dogs were screened for IgG Abs to 10 glycolipids and their 1 : 1 heteromeric complexes using combinatorial glycoarrays. Anti-GM2 ganglioside Abs were detected in 14/25 ACP dogs, and anti-GA1 Abs in one further dog. All controls except for one were negative for anti-glycolipid Abs. In this cohort of cases and controls, the glycoarray screen reached a diagnostic sensitivity of 60% and a specificity of 97%; a lower sensitivity (32%) was reported using a conventional glycolipid ELISA. To address the possible pathogenic role for anti-GM2 Abs in ACP, we identified GM2 in canine sciatic nerve by both mass spectrometry and thin layer chromatography overlay. In immunohistological studies, GM2 was localized predominantly to the abaxonal Schwann cell membrane. The presence of anti-GM2 Abs in ACP suggests that it may share a similar pathophysiology with GBS, for which it could thus be considered a naturally occurring animal model.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".