A fast and accurate method of detecting Aleutian mink disease virus in blood and tissues of chronically infected mink
Bibliographic record
Abstract
The objective of this study was to assess the sensitivity of the Omni Klentaq-LA DNA polymerase for detecting Aleutian mink disease virus (AMDV) in mink blood and tissues by PCR without DNA extraction. The presence of AMDV DNA was directly tested by Klentaq in the plasma, serum, whole blood, and spleen homogenates of 188 mink 4 and 16 months after inoculation with the virus. Samples from bone marrow, small intestine, liver, lungs, kidneys, and lymph nodes of 20 of the same mink were also tested by Klentaq. DNA was extracted from paired samples of plasma and the aforesaid tissues by a commercial nucleic acid extraction kit (Dynabeads Silane) and tested by PCR. Compared with the extracted DNA, Klentaq detected a significantly greater number of samples in the whole blood, serum, plasma, spleen, and small intestine. It was concluded that Klentaq is a preferred system for directly detecting AMDV DNA in mink blood and tissues. The lower success rate of extracted DNA compared with Klentaq could be the result of DNA losses during the extraction process. This is an important factor in chronically infected mink, which have a low AMDV copy number in the bloodstream. Direct AMDV detection also reduces the cost of PCR amplification and lowers the risk of sample contamination.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".