Sequence analysis of three canine adipokine genes revealed an association between <i><scp>TNF</scp></i> polymorphisms and obesity in Labrador dogs
Bibliographic record
Abstract
Obesity is an emerging health problem in purebred dogs. Due to their crucial role in energy homeostasis control, genes encoding adipokines are considered candidate genes, and their variants may be associated with predisposition to obesity. Searching for polymorphism was carried out in three adipokine genes (TNF, RETN and IL6). The study was performed on 260 dogs, including lean (n = 109), overweight (n = 88) and obese (n = 63) dogs. The largest cohort was represented by Labrador Retrievers (n = 136). Altogether, 24 novel polymorphisms were identified: 12 in TNF (including one missense SNP), eight in RETN (including one missense SNP) and four in IL6. Distributions of five common SNPs (two in TNF, two in RETN and one in IL6) were further analyzed with regard to body condition score. Two SNPs in the non-coding parts of TNF (c.-40A>C and c.233+14G>A) were associated with obesity in Labrador dogs. The obtained results showed that the studied adipokine genes are highly polymorphic and two polymorphisms in the TNF gene may be considered as markers predisposing Labrador dogs to obesity.
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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 | 0.001 |
| 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".