Bibliographic record
Abstract
The goal of suicide gene therapy is the specific expression of a toxic gene in cancer cells. In order to achieve this objective, vehicles and transfection strategies must be designed to specifically deliver suicide genes to the desired cells. Up to now, the use of bacteria and other micro-organisms, viruses, and nonviral liposomes as well as the transfection of naked DNA have all been explored as methods of introducing foreign DNA into cells. This chapter describes the use of peptides, proteins, and polymers in the formulation of nonviral transfection agents. A number of recent reviews have outlined the advantages and disadvantages of nonviral systems to deliver therapeutic agents ( 1 – 3 ) and genes ( 4 – 9 ). One advantage of these vectors over viral systems is the diversity of agents that can be used. Unlike viruses, which often lack cell specificity, the potential exists to tailor the delivery of peptide- or polymer-based vectors to target cells of interest. On the other hand, viruses are advantageous in that they have evolved to use natural mechanisms to enter cells and their DNA is packaged to induce the expression of foreign genes. The efficient delivery and expression of novel genes can also be achieved with nonviral systems. Many of the agents discussed in this chapter, however, remain under development and are not commercially available. Nonetheless, there exists a great potential for the use of peptide- and polymer-based delivery agents in clinical applications. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.001 |
| 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) | 0.004 | 0.007 |
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".