Efficacy of microbicidal actives and formulations for inactivation of Lassa virus in suspension
Bibliographic record
Abstract
Abstract The World Health Organization’s R&D Blueprint list of priority diseases for 2022 includes Lassa fever, signifying the need for research and development in emergency contexts. This disease is caused by the arenavirus Lassa virus (LASV). Being an enveloped virus, LASV should be susceptible to a variety of microbicidal actives, although empirical data to support this expectation are needed. We evaluated the virucidal efficacy of sodium hypochlorite, ethanol, a formulated dual quaternary ammonium compound, an accelerated hydrogen peroxide formulation, and a p-chloro-m-xylenol formulation, per ASTM E1052-20, against LASV engineered to express green fluorescent protein (GFP). A 10-μL volume of virus in tripartite soil (bovine serum albumin, tryptone, and mucin) was combined with 50 μL of disinfectant in suspension for 0.5, 1, 5, or 10 min at 20–25 °C. Neutralized test mixtures were quantified by GFP expression to determine log10 reduction. Remaining material was passaged on Vero cells to confirm absence of residual infectious virus. Input virus titers of 6.6–8.0 log10 per assay were completely inactivated by each disinfectant within 1–5 min contact time. The rapid and substantial inactivation of LASV suggests the utility of these microbicides for mitigating spread of infectious virus during Lassa fever outbreaks.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".