In vivo MALDI-TOF markers for early detection of Aleutian disease (AD) among the AD virus infected mink
Bibliographic record
Abstract
A significant proportion of infected mink of non-Aleutian genotype naturally resists progression to immune complex disease. The nature of the AD resistance is presumably genetic. While hypergammaglubulinemia can be insensitively detected by an iodine test, more sensitive diagnosis of hyper-γ globulinemia and of other serum protein parameters associated with an immune-complex disease is currently cost prohibitive, time consuming, and laborious. Thus, practical, cost effective, sensitive, and high throughput tests for in vivo markers of disease vs. health in AD virus infected animals are currently not available. Such markers could have potential practical use in selection of AD virus infected animals for disease resistance. In this study, initially, parameters for serum protein MALDI-TOF serum analysis were established, and subsequently serum proteins of infected mink (CIEP positive) and mink from ADV uninfected farms were profiled by MALDI-TOF spectrometry. We determined the albumin-γ globulin (A/γ G) ratios and albumin-C reactive protein (A/C) ratios associated with health and disease, and furthermore we identified a 76Kda protein, associated exclusively with disease, i.e. with pathological A/ γ G ratio. We concluded that MALDI-TOF profiles of CIPE positive animals provide practical and cost effective tool for selection of breeders for AD resistant phenotype, which could in turn facilitate ‘on farm resistance breeding programs’.
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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 | 0.000 |
| 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".