<em>Ex Vivo</em> Infection of Live Tissue with Oncolytic Viruses
Bibliographic record
Abstract
Oncolytic Viruses (OVs) are novel therapeutics that selectively replicate in and kill tumor cells(1). Several clinical trials evaluating the effectiveness of a variety of oncolytic platforms including HSV, Reovirus, and Vaccinia OVs as treatment for cancer are currently underway(2-5). One key characteristic of oncolytic viruses is that they can be genetically modified to express reporter transgenes which makes it possible to visualize the infection of tissues by microscopy or bio-luminescence imaging(6,7). This offers a unique advantage since it is possible to infect tissues from patients ex vivo prior to therapy in order to ascertain the likelihood of successful oncolytic virotherapy(8). To this end, it is critical to appropriately sample tissue to compensate for tissue heterogeneity and assess tissue viability, particularly prior to infection(9). It is also important to follow viral replication using reporter transgenes if expressed by the oncolytic platform as well as by direct titration of tissues following homogenization in order to discriminate between abortive and productive infection. The object of this protocol is to address these issues and herein describes 1. The sampling and preparation of tumor tissue for cell culture 2. The assessment of tissue viability using the metabolic dye alamar blue 3. Ex vivo infection of cultured tissues with vaccinia virus expressing either GFP or firefly luciferase 4. Detection of transgene expression by fluorescence microscopy or using an In Vivo Imaging System (IVIS) 5. Quantification of virus by plaque assay. This comprehensive method presents several advantages including ease of tissue processing, compensation for tissue heterogeneity, control of tissue viability, and discrimination between abortive infection and bone fide viral replication.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".