Bibliographic record
Abstract
DNA vaccines have been used to generate protective immunity against tumors in a variety of experimental models. The favorite target antigens have been those that are frequently expressed by human tumors, such as carcinoembryonic antigen (CEA), ErbB2/neu, and melanoma-associated antigens. DNA vaccines have the advantage of being simple to construct, produce and deliver. They can activate all arms of the immune system, and allow substantial flexibility in modifying the type of immune response generated through codelivery of cytokine genes. DNA vaccines can be applied by intramuscular, dermal/epidermal, oral, respiratory and other routes, and pose relatively few safety concerns. Compared to other nucleic acid vectors, they are usually devoid of viral or bacterial antigens and can be designed to deliver only the target tumor antigen(s). This is likely to be important when priming a response against weak tumor antigens. DNA vaccines have been more effective in rodents than in larger mammals or humans. However, a large number of methods that might be applied clinically have been shown to ameliorate these vaccines. This includes in vivo electroporation, and/or inclusion of various immunostimulatory molecules, xenoantigens (or their epitopes), antigen-cytokine fusion genes, agents that improve antigen uptake or presentation, and molecules that activate innate immunity mechanisms. In addition, CpG motifs carried by plasmids can overcome the negative effects of regulatory T cells. There have been few studies in humans, but recent clinical trials suggest that plasmid/virus, or plasmid/antigen-adjuvant, prime-boost strategies generate strong immune responses, and confirm the usefulness of plasmid-based vaccination.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".