Nanoparticle-Mediated Interleukin-12 Cancer Gene Therapy
Bibliographic record
Abstract
Interleukin-12 (Il-12) is a heterodimeric cytokine which has been proven to possess antitumor effects in various animal models via stimulating the immune system. However, the main problem associated with Il-12 protein delivery is its instability as well as cytotoxicity subsequent to systemic administration in rodents and in clinical trials. However, gene delivery can be used to deliver genes of interest to the tumor site. Hence, a large number of studies have been undertaken to deliver genes of interest to the tumor site through viral or non-viral vectors. Viral DNA delivery systems suffer from safety concern due to the toxicity of the viruses and strong immune response, while non-viral gene delivery systems proffer lower transfection efficiency. In contrast, nanometer-sized complexes of therapeutic DNA may prove to be more efficient for administration of therapeutic genes to solid tumors compared to administration of naked plasmid DNA. Nanoparticle-based gene delivery systems might be more pertinent, due to enhanced tissue penetrability, and improved cellular uptake. Il-12 gene delivery has already been reported with different nanoparticles containing DNA. This article provides a review on the in vivo and in vitro studies using various nanoparticles, for delivery of the Il-12 gene to neoplastic cells. The future of these promising approaches lies in the development of better techniques for preparing Il-12 gene delivery systems with complete efficiency of viral vectors in addition to the highest safety for cancer patients.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".