Reduced severity of histopathological lesions in mink selected for tolerance to Aleutian mink disease virus infection
Bibliographic record
Abstract
The objective of this study was to measure the effect of selection for tolerance on the severity of the Aleutian disease (AD) lesions in mink. Sensitivity and specificity of antibody detection in the blood by counter-immunoelectrophoresis (CIEP) relative to the presence of Aleutian mink disease virus (AMDV) in the spleen by PCR in naturally infected farmed mink were also estimated. Carcasses of 680 sero-positive (CIEP-P) black mink from 28 farms in Nova Scotia, Canada, and from 132 sero-negative (CIEP-N) mink from 14 of these farms were collected at pelting time. A total of 116 of the CIEP-P mink were from three farms where animals have been selected for tolerating AD for almost 20years. The severity of the AD lesions was assessed by histopathological examination of kidneys, lungs, heart, brain and liver on a scale of 0 to 4. Sensitivity and specificity of CIEP relative to PCR were 0.97 and 0.85, respectively, and 16.5% of CIEP-N mink were PCR positive, which could be one of the reasons for the failure of virus eradication by CIEP in Canada. The CIEP-N and tolerant CIEP-P animals had 9.39 and 6.23 greater odds of showing lower lesion severity, respectively, than the CIEP-P animals (P<0.01). The CIEP-N mink had a slightly higher chance (P=0.07) of showing lower lesion severity (odds ratio 1.51) compared with tolerant CIEP-P mink. The results suggested that tolerant mink had significantly reduced severity of AD lesions despite having anti-viral antibodies and carrying the virus.
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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 | 0.000 | 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".