A novel approach for scrapie-associated prion (PrPSc) detection in blood using the competitive affinity of an aggregate-specific antibody and streptavidin to PrPSc
Bibliographic record
Abstract
Scrapie is a fatal neurodegenerative disorder affecting sheep and goats, originating from exposure to disease-associated prions (PrPSc). An ante-mortem screening test that can detect native PrPSc in body fluids remains unavailable due to insufficient sensitivity of current detection methods that involve proteinase or denaturation treatments. We adopted an approach to detect PrPSc in whole blood using a simple proteinase- and denaturation-independent immunoassay, based on the competitive affinity of an aggregate-specific monoclonal antibody and streptavidin to PrPSc. First, we demonstrated the ability of native PrPSc to bind to streptavidin and the inhibition of this interaction by 15B3 antibody (P < 0.05). This led to a new two-step assay that involved capturing native prions from infected blood on a solid-state matrix and detection of PrPSc aggregates by evaluating the conformation-dependent conjugate catalytic activity ratio in samples against a pre-determined threshold. This test showed capacity for detecting scrapie prions in 500 μl of sheep whole blood spiked with scrapie brain homogenate containing approximately 5 ng of total brain protein, and estimated to have 500 fg of PrPSc. The test also discriminated between blood samples from scrapie-negative (6 sheep, 4 goats) and scrapie-infected animals (3 experimentally infected sheep, 7 naturally infected goats). Collectively, with the proposed high-throughput sample-processing platform, these initial studies provide insights into the development of a large-scale screening test for the routine diagnosis of scrapie.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".