Immune Modulation by Plasmid DNA-mediated Cytokine Gene Transfer
Bibliographic record
Abstract
Autoimmune diseases frequently develop as a result of an abnormal activation of autoreactive T cells, excessive production of proinflammatory cytokines, particularly by CD4(+) Th1 cells, and subsequent tissue destruction. Cytokine-dependent immunotherapy can be applied to alter the balance between Th1 and Th2 cell activity, or proinflammatory versus immunosuppressive cytokine profiles. Cytotoxic T lymphocyte (CTL) and/or macrophage activity can also be suppressed. Gene transfer offers numerous advantages for the in vivo delivery of cytokines or their receptors for immunotherapeutic use. We have relied on the injection of naked plasmid DNA into skeletal muscle to deliver therapeutic genes. In particular, we have successfully used this approach to deliver neutralizing cytokine receptors such as interferon gamma (IFNgamma)-receptor-Ig fusion proteins or anti-inflammatory cytokines such as transforming growth factor beta-1 (TGF-beta1) and interleukin 4 (IL-4). Intramuscular gene therapy is effective in protecting against several experimental autoimmune diseases including insulin-dependent diabetes mellitus (IDDM), experimental allergic encephalomyelitis (EAE), and systemic lupus erythematosus (SLE). Another promising approach involves DNA vaccination by plasmid-based codelivery of genes encoding an autoantigen and either a cytokine or other immunomodulatory molecule. Plasmid vectors offer interesting advantages over viral vectors, since they are simple to produce, non-immunogenic and non-pathogenic. They can be repeatedly administered with relatively prolonged periods of expression in vivo, ranging from weeks to months after each injection. Plasmid-based intramuscular gene transfer has great therapeutic potential in the areas of autoimmune and inflammatory disorders.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".