Possibilities of skin coat color-dependent risks and risk factors of squamous cell carcinoma and deafness of domestic cats inferred via RNA-seq data
Bibliographic record
Abstract
Abstract The transcriptome data of skin cells from domestic cats with brown, orange, and white coats were analyzed using a public database to investigate the possible relationship between coat color-related gene expression and squamous cell carcinoma risk, as well as the mechanism of deafness in white cats. We found that the ratio of the expression level of genes suppressing squamous cell carcinoma to that of genes promoting squamous cell carcinoma might be considerably lower than the theoretical estimation in skin cells with orange and white coats in white-spotted cat. We also found the possibility of the frequent production of KIT lacking the first exon (d1KIT) in skin cells with white coats, and d1KIT production exhibited a substantial negative correlation with the expression of SOX10, which is essential for melanocyte formation and adjustment of hearing function. Additionally, the production of d1KIT was expected to be due to the insulating activity of the feline endogenous retrovirus 1 (FERV1) LTR in the 1st intron of KIT and its CTCF binding sequence repeat. These results contribute to basic veterinary research to understand the relationship between cat skin coat and disease risk, as well as the underlying mechanism.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".