Biodegradation of Prions in Compost
Bibliographic record
Abstract
Composting may serve as a practical and economical means of disposing of specified risk materials (SRM) or animal mortalities potentially infected with prion diseases (transmissible spongiform encephalopathies, TSE). Our study investigated the degradation of prions associated with scrapie (PrP(263K)), chronic waste disease (PrP(CWD)), and bovine spongiform encephalopathy (PrP(BSE)) in lab-scale composters and PrP(263K) in field-scale compost piles. Western blotting (WB) indicated that PrP(263K), PrP(CWD), and PrP(BSE) were reduced by at least 2 log10, 1-2 log10, and 1 log10 after 28 days of lab-scale composting, respectively. Further analysis using protein misfolding cyclic amplification (PMCA) confirmed a reduction of 2 log10 in PrP(263K) and 3 log10 in PrP(CWD). Enrichment for proteolytic microorganisms through the addition of feather keratin to compost enhanced degradation of PrP(263K) and PrP(CWD). For field-scale composting, stainless steel beads coated with PrP(263K) were exposed to compost conditions and removed periodically for bioassays in Syrian hamsters. After 230 days of composting, only one in five hamsters succumbed to TSE disease, suggesting at least a 4.8 log10 reduction in PrP(263K) infectivity. Our findings show that composting reduces PrP(TSE), resulting in one 50% infectious dose (ID50) remaining in every 5600 kg of final compost for land application. With these considerations, composting may be a viable method for SRM disposal.
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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 | 0.000 | 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".