From Scourge to Cure: Tumour-Selective Viral Pathogenesis as a New Strategy against Cancer
Bibliographic record
Abstract
Tumour mutations corrupt cellular pathways, and accumulate to disrupt, dysregulate, and ultimately avoid mechanisms of cellular control. Yet the very changes that tumour cells undergo to secure their own growth success also render them susceptible to viral infection. Enhanced availability of surface receptors, disruption of antiviral sensing, elevated metabolic activity, disengagement of cell cycle controls, hyperactivation of mitogenic pathways, and apoptotic avoidance all render the malignant cell environment highly supportive to viral replication. The therapeutic use of oncolytic viruses (OVs) with a natural tropism for infecting and subsequently lysing tumour cells is a rapidly progressing area of cancer research. While many OVs exhibit an inherent degree of tropism for transformed cells, this can be further promoted through pharmacological interventions and/or the introduction of viral mutations that generate recombinant oncolytic viruses adapted to successfully replicate only in a malignant cellular environment. Such adaptations that augment OV tumour selectivity are already improving the therapeutic outlook for cancer, and there remains tremendous untapped potential for further innovation.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".