Application of Bioluminescence Imaging to the Prediction of Lethality in Vaccinia Virus-Infected Mice
Bibliographic record
Abstract
To find an alternative endpoint for the efficacy of antismallpox treatments, bioluminescence was measured in live BALB/c mice following lethal challenge with a recombinant WR vaccinia virus expressing luciferase. Intravenous vaccinia immunoglobulin treatments were used to confer protection on a proportion of animals. Using known lethality outcomes in 200 animals and total fluxes recorded daily in live animals, we performed univariate receiver operating characteristic (ROC) curve analysis to assess whether lethality can be predicted based on bioluminescence. Total fluxes in the spleens on day 3 and in the livers on day 5 generated accurate predictive models; the area under the ROC curve (AUC) was 0.91. Multiple logistic regression analysis utilizing a linear combination of six measurements: total flux in the liver on days 2, 3, and 5; in the spleen on days 1 and 3; and in the nasal cavity on day 4 generated the most accurate predictions (AUC = 0.96). This model predicted lethality in 90% of animals with only 10% of nonsurviving animals incorrectly predicted to survive. Compared with bioluminescence, ROC analysis with 25% and 30% weight loss as thresholds accurately predicted survival on day 5, but lethality predictions were low until day 9. Collectively, our data support the use of bioimaging for lethality prediction following vaccinia virus challenge and for gaining insight into protective mechanisms conferred by vaccines and therapeutics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".