Adenoviral Vectors Armed with Cell Fusion-Inducing Proteins as Anti-Cancer Agents
Bibliographic record
Abstract
Cancer is a devastating disease that affects millions of patients every year, and causes an enormous economic burden on the health care system and emotional burden on affected families. The first line of defense against solid tumors is usually extraction of the tumor, when possible, by surgical methods. In cases where solid tumors can not be safely removed, chemotherapy is often the first line of treatment. As metastatic cancers often become vigorously resistant to treatments, the development of novel, more potent and selective anti-cancer strategies is of great importance. Adenovirus (Ad) is the most commonly used virus in cancer clinical trials, however, regardless of the nature of the Ad-based therapeutic, complete responses to treatment remain rare. A number of pre-clinical studies have shown that, for all vector systems, viral spread throughout the tumor mass can be a major limiting factor for complete tumor elimination. By expressing exogenous cell-fusion proteins, many groups have shown improved spread of Ad-based vectors. This review summarizes the research done to examine the potency of Ad vectors expressing fusogenic proteins as anti-cancer therapeutics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.002 |
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".