Progression of chronic wasting disease in white-tailed deer analyzed by serial biopsy RT-QuIC and immunohistochemistry
Bibliographic record
Abstract
Chronic wasting disease (CWD) continues to spread or be recognized in the United States, Canada, and Europe. CWD is diagnosed by demonstration of the causative misfolded prion protein (PrPCWD) in either brain or lymphoid tissue using immunodetection methods, with immunohistochemistry (IHC) recognized as the gold standard. In recent years, in vitro amplification assays have been developed that can detect CWD prion seeding activity in tissues, excreta, and body fluids of affected cervids. These methods potentially offer earlier and more facile detection of CWD, both pre- and post-mortem. Here we provide a longitudinal profile of CWD infection progression, as assessed by both real-time quaking-induced conversion (RT-QuIC) and IHC on serial biopsies of mucosal lymphoid tissues of white-tailed deer orally exposed to low doses of CWD prions. We report that detection of CWD infection by RT-QuIC preceded that by IHC in both tonsil and recto-anal lymphoid tissue (RAMALT) in 14 of 19 deer (74%). Of the 322 biopsy samples collected in post-exposure longitudinal monitoring, positive RT-QuIC results were obtained for 146 samples, 91 of which (62%) were concurrently also IHC-positive. The lower frequency of IHC positivity was manifest most in the earlier post-exposure periods and in biopsies in which lymphoid follicles were not detected. For all deer in which RT-QuIC seeding activity was detected in a tonsil or RAMALT biopsy, PrPCWD was subsequently or concurrently detected by IHC. Overall, this study (a) provides a longitudinal profile of CWD infection in deer after low yet infectious oral prion exposure; (b) illustrates the value of RT-QuIC for sensitive detection of CWD; and (c) demonstrates an ultimate high degree of correlation between RT-QuIC and IHC positivity as CWD infection progresses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".