Susceptibility of cell substrates to PrP<sup>Sc</sup>infection and safety control measures related to biological and biotherapeutical products
Bibliographic record
Abstract
Concerns over the potential for infectious prion proteins to contaminate human biologics and biotherapeutics have been raised from time to time. Transmission of the pathogenic form of prion protein (PrP(Sc)) through veterinary vaccines has been observed, yet no human case through the use of vaccine products has been reported. However, iatrogenic transmissions of PrP(Sc) in humans through blood components, tissues and growth hormone have been reported. These findings underscore the importance of reliable detection or diagnostic methods to prevent the transmission of prion diseases, given that the number of asymptomatic infected individuals remains unknown, the perceived incubation time for human prion diseases could be decades, and no cure of the diseases has been found yet. A variety of biochemical and molecular methods can selectively concentrate PrP(Sc) to facilitate its detection in tissues and cells. Furthermore, some methods routinely used in the manufacturing process of biological products have been found to be effective in reducing PrP(Sc) from the products. Questions remain unanswered as to the validation criteria of these methods, the minimal infectious dose of the PrP(Sc) required to cause infection and the susceptibility of cells used in gene therapy or the manufacturing process of biological products to PrP(Sc) infections. Here, we discuss some of these challenging issues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".