Visualizing Oncolytic Virus-Host Interactions in Live Mice Using Intravital Microscopy
Bibliographic record
Abstract
Oncolytic virus (OV) therapy is an emerging cancer treatment that uses replicating viruses to infect and kill tumor cells and incite anticancer immunity. While the approach shows promise, it currently fails most patients, indicating strategies to improve OV activity are needed. Developing these will require greater understanding of OV biology, particularly in the context of OV delivery and clearance, the infection process within a complex tumor microenvironment, and the modulation of anticancer immunity. To help achieve this, we have established a technique for high-resolution 4D imaging of OV-host interactions within intact tissues of live mice using intravital microscopy (IVM). We show that oncolytic vesicular stomatitis virus (VSV) directly labeled with Alexa Fluor dyes is easily visualized by single- or multiphoton microscopy while retaining bioactivity in vivo . The addition of fluorophore-tagged antibodies and genetically encoded reporter proteins to image target cells and the virus infection enables real-time imaging of dynamic interactions between VSV and host cells in blood, tumor, and visceral organs of live mice. The method has sufficient in vivo resolution to observe leukocytes in blood binding to and transporting VSV particles, foci of VSV infection spreading through a tumor, and antigen-presenting cells in the spleen interacting with and being infected by VSV. Visualizing OV-host interactions by IVM represents a powerful new tool for studying OV therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".