Investigating the effect of reduced temperatures on the efficacy of rhabdovirus-based viral vector platforms
Bibliographic record
Abstract
Rhabdoviral vectors can induce lysis of cancer cells. While studied almost exclusively at 37 °C, viruses are subject to a range of temperatures in vivo, including temperatures ≤31 °C. Despite potential implications, the effect of temperatures <37 °C on the performance of rhabdoviral vectors is unknown. We investigated the effect of low anatomical temperatures on two rhabdoviruses, vesicular stomatitis virus (VSV) and Maraba virus (MG1). Using a metabolic resazurin assay, VSV- and MG1-mediated oncolysis was characterized in a panel of cell lines at 28, 31, 34 and 37 °C. The oncolytic ability of both viruses was hindered at 31 and 28 °C. Cold adaptation of both viruses was attempted as a mitigation strategy. Viruses were serially passaged at decreasing temperatures in an attempt to induce mutations. Unfortunately, the cold-adaptation strategies failed to potentiate the oncolytic activity of the viruses at temperatures <37 °C. Interestingly, we discovered that viral replication was unaffected at low temperatures despite the abrogation of oncolytic activity. In contrast, the proliferation of cancer cells was reduced at low temperatures. Equivalent oncolytic effects could be achieved if cells at low temperatures were treated with viruses for longer times. This suggests that rhabdovirus-mediated oncolysis could be compromised at low temperatures in vivo where therapeutic windows are limited.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".