Highly Efficient Amplification of Chronic Wasting Disease Agent by Protein Misfolding Cyclic Amplification with Beads (PMCAb)
Bibliographic record
Abstract
Protein misfolding cyclic amplification (PMCA) has emerged as an important technique for detecting low levels of pathogenic prion protein in biological samples. The method exploits the ability of the pathogenic prion protein to convert the normal prion protein to a proteinase K-resistant conformation. Inclusion of Teflon® beads in the PMCA reaction (PMCAb) has been previously shown to increase the sensitivity and robustness of detection for the 263 K and SSLOW strains of hamster-adapted prions. Here, we demonstrate that PMCAb with saponin dramatically increases the sensitivity of detection for chronic wasting disease (CWD) agent without compromising the specificity of the assay (i.e., no false positive results). Addition of Teflon® beads increased the robustness of the PMCA reaction, resulting in a decrease in the variability of PMCA results. Three rounds of serial PMCAb allowed detection of CWD agent from a 6.7 × 10(-13) dilution of 10% brain homogenate (1.3 fg of source brain). Titration of the same brain homogenate in transgenic mice expressing cervid prion protein (Tg(CerPrP)1536(+/-) mice) allowed detection of CWD agent from the 10(-6) dilution of 10% brain homogenate. PMCAb is, thus, more sensitive than bioassay in transgenic mice by a factor exceeding 10(5). Additionally, we are able to amplify CWD agent from brain tissue and lymph nodes of CWD-positive white-tailed deer having Prnp alleles associated with reduced disease susceptibility.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".