Gene therapy of autoimmune diseases with vectors encoding regulatory cytokines or inflammatory cytokine inhibitors
Bibliographic record
Abstract
Gene therapy offers advantages for the immunotherapeutic delivery of cytokines or their inhibitors. After gene transfer, these mediators are produced at relatively constant, non-toxic levels and sometimes in a tissue-specific manner, obviating limitations of protein administration. Therapy with viral or nonviral vectors is effective in several animal models of autoimmunity including Type 1 diabetes mellitus (DM), experimental allergic encephalomyelitis (EAE), systemic lupus erythematosus (SLE), colitis, thyroiditis and various forms of arthritis. Genes encoding transforming growth factor beta, interleukin-4 (IL-4) and IL-10 are most frequently protective. Autoimmune/ inflammatory diseases are associated with excessive production of inflammatory cytokines such as IL-1, IL-12, tumor necrosis factor alpha (TNFalpha) and interferon gamma (IFNgamma). Vectors encoding inhibitors of these cytokines, such as IL-1 receptor antagonist, soluble IL-1 receptors, IL-12p40, soluble TNFalpha receptors or IFNgamma-receptor/IgG-Fc fusion proteins are protective in models of either arthritis, Type 1 DM, SLE or EAE. We use intramuscular injection of naked plasmid DNA for cytokine or anticytokine therapy. Muscle tissue is accessible, expression is usually more persistent than elsewhere, transfection efficiency can be increased by low-voltage in vivo electroporation, vector administration is simple and the method is inexpensive. Plasmids do not induce neutralizing immunity allowing repeated administration, and are suitable for the treatment of chronic immunological diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".