Real-time Quaking-induced Conversion Assay for Detection of CWD Prions in Fecal Material
Bibliographic record
Abstract
The RT-QuIC technique is a sensitive in vitro cell-free prion amplification assay based mainly on the seeded misfolding and aggregation of recombinant prion protein (PrP) substrate using prion seeds as a template for the conversion. RT-QuIC is a novel high-throughput technique which is analogous to real-time polymerase chain reaction (PCR). Detection of amyloid fibril growth is based on the dye Thioflavin T, which fluoresces upon specific interaction with ᵦ-sheet rich proteins. Thus, amyloid formation can be detected in real time. We attempted to develop a reliable non-invasive screening test to detect chronic wasting disease (CWD) prions in fecal extract. Here, we have specifically adapted the RT-QuIC technique to reveal PrPSc seeding activity in feces of CWD infected cervids. Initially, the seeding activity of the fecal extracts we prepared was relatively low in RT-QuIC, possibly due to potential assay inhibitors in the fecal material. To improve seeding activity of feces extracts and remove potential assay inhibitors, we homogenized the fecal samples in a buffer containing detergents and protease inhibitors. We also submitted the samples to different methodologies to concentrate PrPSc on the basis of protein precipitation using sodium phosphotungstic acid, and centrifugal force. Finally, the feces extracts were tested by optimized RT-QuIC which included substrate replacement in the protocol to improve the sensitivity of detection. Thus, we established a protocol for sensitive detection of CWD prion seeding activity in feces of pre-clinical and clinical cervids by RT-QuIC, which can be a practical tool for non-invasive CWD diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".