Cancer Gene Therapy by Adenovirus-Mediated Gene Transfer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 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 it