Inactivation of Zika virus in plasma and derivatives by four different methods
Bibliographic record
Abstract
Abstract Zika virus (ZIKV) is an emerging arbovirus with increasing prevalence in recent years. To reduce the risk of ZIKV transmission by transfusion, some mitigation strategies were recommended based on pathogen reduction technologies for blood products. In this study, we aimed to study the efficacy of several common pathogen reduction methods in the inactivation of ZIKV. The fresh frozen plasma and derivatives were spiked with a high titer of ZIKV or Sindbis virus (SINV). Viral titers and ZIKV RNA were measured before and after the inactivation treatment by methylene blue (MB), solvent/detergent (S/D), pasteurization, and low pH. The mean ZIKV infectivity titers in plasma and derivatives were 7.08 ± 0.14, 5.17 ± 0.14, 7.08 ± 0.14, and 5.80 ± 0.14 log10TCID50/mL, respectively before MB, S/D, pasteurization, and low pH inactivation. We found no detectable ZIKV RNA after five successive passages of inoculation on host cells, indicating there is no infectivity after inactivation. Similar inactivation results were observed for SINV. In conclusion, we achieved robust ZIKV inactivation through the four inactivation procedures in several blood products. These findings suggest that the pathogen reduction technologies commonly applied in plasma and derivatives have the capacity to mitigate the risk of ZIKV transmission by transfusion.
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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".