Detection of Bovine Central Nervous System Tissues in Rendered Animal By-Products by One-Step Real-Time Reverse Transcription PCR Assay
Bibliographic record
Abstract
Contamination of rendered animal byproducts with central nervous system tissues (CNST) from animals with bovine spongiform encephalopathy is considered one of the vehicles of disease transmission. Removal from the animal feed chain of CNST originated from cattle of a specified age category, species-labeling of rendered meat products, and testing of rendered products for bovine CNST are tasks associated with the epidemiological control of bovine spongiform encephalopathy. A single-step TaqMan real-time reverse transcriptase (RRT) PCR assay was developed and evaluated for specific detection of bovine glial fibrillary acidic protein (GFAP) mRNA, a biomarker of bovine CNST, in rendered animal by-products. An internal amplification control, mammalian b -actin mRNA, was coamplified in the duplex RRT-PCR assay to monitor amplification efficiency, normalize amplification signals, and avoid false-negative results. The functionality of the GFAP mRNA RRT-PCR was assessed through analysis of laboratory-generated binary mixtures of bovine central nervous system (CNS) and muscle tissues treated under various thermal settings imitating industrial conditions. The assay was able to detect as low as 0.05 % (wt/wt) bovine brain tissue in binary mixtures heat treated at 110 to 130°C for 20 to 60 min. Further evaluation of the GFAP mRNA RRT-PCR assay involved samples of industrial rendered products of various species origin and composition obtained from commercial sources and rendering plants. Low amounts of bovine GFAP mRNA were detected in several bovine-rendered products, which was in agreement with declared species composition. An accurate estimation of CNS tissue content in industrial-rendered products was complicated due to a wide range of temperature and time settings in rendering protocols. Nevertheless, the GFAP mRNA RRT-PCR assay may be considered for bovine CNS tissue detection in rendered products in combination with other available tools (for example, animal age verification) in inspection 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".