Bibliographic record
Abstract
Despite many advances in chemotherapy and other medical techniques, patients with cancer often develop local recurrence or metastatic spread. Recent advances in molecular biology and tumor immunology have led to the design of many new anti-tumor vaccines. Such approaches are now using recombinant viruses to treat different types of cancer. From these new developments, innovative fields are emerging: vaccine virotherapy, viral immunotherapy, oncolytic virotherapy and drug virotherapy. Many viruses are currently exploited as recombinant vectors and each offers natural or synthetic characteristics that may provide unique means to treat cancer. Poxviruses are large double stranded DNA viruses that offer many advantageous characteristics as recombinant vectors. Poxvirus-based vectors offer essentially unlimited possibilities for genetic manipulation due to the large size of their DNA and high degree of safety. Vaccinia virus, the prototype virus of the Orthopoxvirus genus that was extensively used to eradicate Smallpox, and other poxviruses are now being considered and used for the treatment of cancer. This review will cover their utilization as anti-cancer therapeutics by describing recent patents (2000-2005).
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".