Elevated levels of IL-12/IL-23p40 in Nova Scotia Duck Tolling Retrievers with autoimmune disease and lymphoma
Bibliographic record
Abstract
The Nova Scotia Duck Tolling Retriever (NSDTR) is predisposed to immune mediated rheumatic disease (IMRD), steroid-responsive meningitis-arteritis (SRMA) and certain forms of cancer. Cytokines are the main regulators of the immune system. Interleukin 2 is a cytokine involved in activation of T regulatory cells, playing a role in central tolerance and tumor immunity. Interleukin 12 and interleukin 23 share the same subunit, p40, and are both pro-inflammatory cytokines. The aim of this study was to compare levels of IL-2 in healthy NSDTRs to those with cancer or autoimmune disease and to compare levels of IL-12/IL-23p40 in healthy NSDTRs and beagles versus NSDTRs with cancer or autoimmune disease. 62 dogs were included in the analysis of IL-12/IL-23p40; healthy NSDTRs (n = 16), healthy beagles (n = 16), NSDTRs autoimmune (n = 18) and NDSTRs lymphoma/mastocytoma (n = 12) and 68 dogs for IL-2; healthy (n = 20), autoimmune (n = 36) and lymphoma/mastocytoma/adenocarcinoma (n = 12). NSDTRs with autoimmune disease had higher levels of IL-12/IL-23p40 compared to healthy dogs (p = 0.008). NSDTRs with lymphoma also had higher levels of IL-12/IL-23p40 compared to healthy NSDTRs (p = 0.002). There was no difference in levels of IL-2 between healthy and diseased NSDTR. Statistical analysis was performed using Bonferroni corrections for multiple testing. These findings can contribute to the knowledge of autoimmune disease and cancer in 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 | 0.000 | 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".