Cancer Gene Therapy by Adenovirus-Mediated Gene Transfer
Bibliographic record
Abstract
Cancer arises as a direct result of genetic mutations. It therefore stands to reason that cancer should be well suited for the correction through gene therapy. Recent advances in the understanding of the molecular pathogenesis of cancer and the rapid development of recombinant DNA technology have made cancer gene therapy feasible in the clinical setting. The current efforts for cancer gene therapy mainly focus on immunogene therapy, chemogene therapy, restoration of tumor suppressor gene function, and oncolytic virus therapy. Central to all these therapies is the development of efficient vectors for gene delivery--this remains a work in progress. These vectors can be classified as viral and non-viral vectors. This paper will concentrate on viral vectors because of their practical advantages over non-viral vectors. Of the viral vectors, by far the most important are the human adenoviruses as is reflected by the enormous data and literature accumulated by studies relating to animal tumor models and clinical trials. In this review, we examine the recent progress in adenovirus-mediated cancer gene therapy with regard to cytokine gene, tumor suppressor gene, chemogene, and oncolytic adenovirus. We also discuss the current limitations of the adenoviral vector system and how they may be circumvented in future developments relating to targeted gene delivery.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.003 |
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".