Epidemiology, pathogenesis, and diagnosis of Aleutian disease caused by Aleutian mink disease virus: A literature review with a perspective of genomic breeding for disease control in American mink (Neogale vison)
Bibliographic record
Abstract
Aleutian disease (AD) is a multi-systemic infectious disease in American mink (Neogale vison) caused by the Aleutian mink disease virus (AMDV). Commonly referred to as mink plasmacytosis, AD is an economically significant disease in mink-breeding countries. Aleutian disease mainly induces weight loss, lower fertility, and dropped pelt quality in adults and can result in acute interstitial pneumonia with high mortality rates in kits. In this review, we employed the scientific literature on AD over the last 70 years to discuss the historical and contemporary status of AD outbreaks and seroprevalence in mink farming countries. We also explained different forms of AD and the differences between the pathogenicity of the virus in kits and adults. The application of the available AD serological tests in AD control strategies was argued. We explained how selection programs could help AD control and proposed different approaches to selecting animals for building AD-tolerant herds. The advantages of genomic selection for AD tolerance over traditional breeding strategies were discussed in detail. We also explained how genomic selection could help AD control by selecting tolerant animals for the next generation based on genome-wide single nucleotide polymorphisms (SNP) data and the challenges of implementing genomic selection for AD tolerance in the mink industry. This review collected the information required for designing successful breeding programs for AD tolerance. Examples of the application of information are presented, and data gaps are highlighted. We showed that AD tolerance is necessary to be among the traits that animals are selected for in the mink industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".