A direct relationship between the partitioning of the pathogenic prion protein and transmissible spongiform encephalopathy infectivity during the purification of plasma proteins
Bibliographic record
Abstract
BACKGROUND: Experimental evidence from rodent models indicates that blood can contain transmissible spongiform encephalopathy (TSE) infectivity, which suggests a potential risk for TSE transmission via proteins isolated from human plasma. Because methods that can reduce TSE infectivity typically are detrimental to protein function, infectivity must be removed to ensure the safety of these therapeutic proteins. Animal bioassays are conventionally used to detect infectivity, but the pathogenic form of the prion protein (PrP(Sc)) can serve as a marker for TSE infectivity. STUDY DESIGN AND METHODS: Seven plasma protein-purification steps were performed after the plasma intermediates were spiked with TSE-infected material. Resulting fractions were analyzed for PrP(Sc) by using a Western blot assay and for TSE infectivity by using an animal bioassay. Western blots were quantitated by an endpoint dilution analysis, and infectivity titers were calculated by the Spearman-Kärber method. RESULTS: PrP(Sc) partitioning paralleled TSE infectivity partitioning, regardless of the nature of the protein-purification step. The detection ranges for PrP(Sc) and infectivity were 0 to 5.3 log and 1.1 to 8.9 log median infectious dose per unit, respectively. Clearance of PrP(Sc) and infectivity ranged from 1.0 to 6.0 log. CONCLUSION: Purification steps for isolating therapeutic proteins from human plasma showed the removal of both PrP(Sc) and TSE infectivity. PrP(Sc) partitioning coincided with infectivity partitioning, which showed a close relationship between PrP(Sc) and TSE infectivity. By exploiting this association, the in vitro Western blot assay for PrP(Sc) was valuable for estimating the partitioning of TSE infectivity during plasma protein purification.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".