Assessment of Toll-like receptor 2, 4 and 9 SNP genotypes in canine sino-nasal aspergillosis
Bibliographic record
Abstract
BACKGROUND: The exact aetiology of canine sino-nasal aspergillosis (SNA) is unknown. In man, dysfunction in innate immunity, particularly in the function of pattern recognition receptors, is implicated in the pathogenesis of inflammatory sino-nasal disease and in fungal diseases. Associations between single nucleotide polymorphisms (SNPs) in Toll-like receptors (TLRs) and these diseases have been identified. Similarly, in dogs SNPs in genes encoding TLRs may be important in the pathogenesis of SNA. The aims of the present study were (1) to identify the presence of non-synonymous SNPs in the coding regions of the TLR2, 4 and 9 genes in dogs suffering from SNA, and (2) to investigate the SNP genotypes in dogs with SNA compared with a control population. RESULTS: Direct sequencing of nine dogs of various breeds with SNA revealed two non-synonymous SNPs in the coding region of TLR2, eight in TLR4 and four in TLR9. These non-synonymous SNPs were further evaluated in a case-control study of affected Golden Retrievers, Labrador Retrievers, Rottweilers and Beaucerons. Genotyping was performed using a combination of allele-specific primers and hydrolysis probe assays in 31 dogs with SNA and 31 controls. No significant difference in minor allele frequency was identified between these groups, for all studied SNPs, in any of the four breeds. CONCLUSIONS: These findings do not support a role for non-synonymous SNPs in the TLR 2, 4 and 9 coding regions in the pathogenesis of canine SNA, but do not exclude a role for innate immunity in the pathogenesis of the disease.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".