Photoinactivation of MS2 Bacteriophage Is Enhanced by Unrecognized Proteins from Viral Preparations in Clear Suspensions
Bibliographic record
Abstract
Light-mediated inactivation of pathogenic microorganisms is vital to engineering water treatment processes and predicting fate and transport in the environment. However, there is high variability in reported viral photoinactivation rates, even in purported sensitizer-free suspensions. Using bacteriophage MS2 as a model, this study explores the impact of progressive inoculant purification processing by centrifugation, filtration, and ultrafiltration, on photoinactivation rates. Faster inactivation kinetics under UVB radiation correlated with greater presence of background proteins as identified via sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Differences in the determined rate constants were large and statistically significant, nearing a 70% increase from the lowest to highest protein presence. Variations remained even after 100× dilution of viral stock preparations. Furthermore, the likely basis was determined to be an indirect inactivation mechanism via sensitization of host or media proteins in the solution background, as evidenced by the inclusion of singlet oxygen scavenger l -histidine, which resulted in a clustering of reaction rates near the ultracentrifuged sample. These experiments highlight the methodological importance of performing and confirming inoculant purification prior to performing photoinactivation experiments, particularly when isolation of the endogenous pathway is important for practical or mechanistic conclusions, while demonstrating the notable contribution of proteins to the exogenous pathway.
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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".